UNDERSTANDING AND PREVENTING

ANTERIOR CRUCIATE LIGAMENT INJURIES

USING NOVEL MOTION ANALYSIS SYSTEMS

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF

MECHANICAL ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Ariel Veronica Dowling May 2011

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/tj428wy3646

© 2011 by Ariel Veronica Dowling. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Thomas Andriacchi, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Mark Cutkosky

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Nicholas Giori

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

iii

~iv~

Abstract The overall goal of this dissertation is to use novel motion analysis systems to

investigate the underlying mechanisms that cause an anterior cruciate ligament (ACL)

injury and then to explore movement modification methods that might prevent ACL

injuries from occurring. This injury causes immediate functional impairment and also

increases the long term risk of developing osteoarthritis (OA), a degenerative joint

disease. Thus, understanding the causes of this injury and investigating methods to

prevent it from occurring are important goals and could lead to improved health and

quality of life. Additionally, novel motion analysis systems can provide new

information about ACL injuries and therefore should be used to help analyze these

injuries from a different perspective. This thesis provides the results from multiple

experimental studies that used two novel motion analysis systems to investigate the

underlying causes of ACL injury and potential injury prevention methods. These

results add to the understanding of the ACL injury mechanism and also suggest

potential preventative methods that could decrease the overall incidence of ACL

injury.

Using a markerless motion capture system, the first investigation determined

that increasing the coefficient of friction of the shoe-surface condition will change a

subject’s movement strategies during a sidestep cutting task in specific ways that may

increase the risk of ACL injury. Additionally, increased running speed combined with

increased floor friction further alters a subject’s movement in biomechanical measures

associated with risk for ACL injury, and these changes are different between females

and males. This investigation provides a biomechanical basis for the increased

incidence of ACL injuries on high friction surfaces, and suggests that the

biomechanical causes change based on the speed of the maneuver. In terms of gender,

this investigation suggests that females are more at risk for ACL injury when cutting

on high friction surfaces at different speeds.

In terms of novel motion analysis systems, there is a need for simple, cost

effective methods to identify athletes at a higher risk for ACL injury during jumping

~v~

tasks. Wearable systems offer many advantages over traditional motion capture

systems: they are simpler to use, do not require complex post-processing, and make it

feasible to test subjects in a natural environment. As such, the second study assessed

the capacity of a wearable inertial-based system to evaluate ACL injury risk during

jumping tasks. This system accurately detected crucial temporal events and measured

total jump height with a precision comparable to dedicated optical devices.

Additionally, the proposed system measured the knee flexion and the trunk lean, and

demonstrated good concurrent validity and discriminative performance in terms of the

known risk factors for ACL injury. This study also reported the angular velocity of the

thigh and shank segments during bilateral and unilateral drop jumps for the first time,

and showed that angular velocity was consistent between subjects. Furthermore, this

study illustrated there is an association between the coronal segment angular velocity

and knee abduction moment, and that the coronal segment angular velocity can

differentiate between subjects at higher risk for ACL injury.

Recent studies have shown that the incidence of ACL injury can be decreased

through the use of intervention programs, but the quality of the feedback provided to

the participants in these programs can vary depending on the skill of the observer.

Therefore, the objective for the final study was to determine if an independent inertial-

based system can be used to modify jump landing mechanics in order to decrease the

risk for ACL injury by providing real-time feedback based on known kinematic and

kinetic injury risk factors. This study found that the subjects reduced their risk for

ACL injury after training with the system because there were significant increases in

the maximum knee flexion angle and the maximum trunk lean. The subjects also

reduced their risk for injury by decreasing their thigh coronal angular velocity, which

was correlated with a decrease in their knee abduction moment. This study suggests

that an inertial-based system could be used for interventional training aimed at

reducing the risk for ACL injury.

~vi~

Acknowledgements

There have been many people who have helped me throughout my time at

Stanford. I would like to start by sincerely thanking my advisor, Tom Andriacchi, for

providing me with guidance, mentorship, and for allowing me to pursue research that

truly excited my passion for science and engineering. He has made me a better

researcher by providing advice and direction to my work while at the same time giving

me the freedom to learn and grow on my own. In a similar vein I would like to extend

a very special thanks to Julien Favre, who has been an amazing mentor, coauthor, and

friend from the day he walked in our office door. His assistance and guidance have

been invaluable, and over the past two years he has helped me to refocus my thesis in

order to make a more impactful contribution to science, to conduct a year-long jump

study extravaganza, and to write many papers and abstracts. I am truly grateful that he

has shared his knowledge and friendship with me. I would also like to thank Ajit

Chaudhari for introducing me to the world of scientific research and for helping me

through my first major study, and to Stefano Corazza for being my office mate and

teaching me about the world of markerless motion capture. I would also like to thank

the many members of the BioMotion Lab, both past and present, for making the last 6

years of my life educational, interesting, and exciting; I truly value all the time I have

spent with everyone and will enjoy keeping up with everyone’s lives in the years to

come.

Furthermore, thank are definitely due to Melinda Cromie and Melanie Fox for

working with me on countless problem sets and projects, providing me with advice on

all things biomechanics, and for being amazing friends since my very first day at

Stanford. I would also like to express my appreciation to all of the subjects (most of

them my friends) that have volunteered their time to participate in my studies at the

Biomotion Lab. Additionally, I would like to thank Stanford University, the Palo Alto

VA, and the NSF as the funding sources for these projects.

Finally, I would like to thank those closest to me for their unwavering love and

support. I would not be the person I am today without my family, Paul, Laurie, and

~vii~

Russell. Their unconditional love and support, as well as their belief that I can

accomplish anything, have given me the confidence to achieve and the knowledge that

I am loved regardless of what I do achieve. I would also like to thank my very special

person Adrienne Diebold, who has been the yin to my yang for almost 10 years. Her

advice has helped me in both science and life, and I am truly grateful to her for sharing

her opinions, advice, workouts, and friendship with me over the last decade. Finally, I

will forever be thankful for my wonderful partner in life, Aron Levin. His steadfast

support, humor, time management skills, and assistance in all aspects of my life have

enriched my life beyond measure. I look forward to our next big adventure together

and all the years to follow. ני אוהבת אותו היום ובכל יוםא

~viii~

Table of Contents Abstract .................................................................................................... iv 

Acknowledgements .................................................................................. vi 

Table of Contents ................................................................................... viii 

List of Tables ........................................................................................... xii 

List of Figures ........................................................................................ xiii 

11  Introduction ......................................................................................... 1 

1.1.  Overview ........................................................................................ 1 

1.2.  Anterior Cruciate Ligament Injury ................................................ 1 1.2.1.  Description ............................................................................... 1 1.2.2.  Prevalence ................................................................................ 3 1.2.3.  Osteoarthritis ............................................................................ 4 

1.3.  Statement of Purpose ..................................................................... 5 1.4.  Outline of Upcoming Chapters ...................................................... 6 

22  Review of Relevant Literature ........................................................... 8 

2.1.  Mechanisms of ACL Injury ........................................................... 8 2.2.  Risk Factors for Injury ................................................................... 9 

2.2.1.  Biomechanical: Kinematics ..................................................... 9 2.2.2.  Biomechanical: Kinetics ........................................................ 10 2.2.3.  Environmental Factors ........................................................... 13 

2.3.  Prevention Strategies and Programs ............................................ 14 2.3.1.  Knee Flexion Angle Modification ......................................... 14 2.3.2.  Real-Time Feedback Modifications ...................................... 15 

2.4.  Novel Motion Analysis Systems .................................................. 15 2.4.1.  Markerless Motion Capture ................................................... 16 2.4.2.  Inertial Sensors ...................................................................... 17 

~ix~

33  Shoe-Surface Friction Influences Movement Strategies During a Sidestep Cutting Task: Implications for Anterior Cruciate Ligament Injury Risk ........................................................................ 19 

3.1.  Overview ...................................................................................... 19 3.2.  Introduction .................................................................................. 20 3.3.  Methods ........................................................................................ 21 

3.3.1.  Subjects .................................................................................. 21 3.3.2.  Experimental Design ............................................................. 22 3.3.3.  Data Collection ...................................................................... 23 3.3.4.  Data Analysis ......................................................................... 24 3.3.5.  Statistical Analysis ................................................................ 25 

3.4.  Results .......................................................................................... 26 3.5.  Discussion .................................................................................... 30 3.6.  Conclusion ................................................................................... 34 3.7.  Acknowledgments ........................................................................ 34 

44  Running Speed and Gender Influence Movement Strategies During a Sidestep Cutting Task on Different Friction Surfaces: Implications for ACL Injury Risk ................................................... 35 

4.1.  Overview ...................................................................................... 35 4.2.  Introduction .................................................................................. 36 4.3.  Methods ........................................................................................ 37 

4.3.1.  Subjects .................................................................................. 37 4.3.2.  Experimental Design ............................................................. 38 4.3.3.  Data Collection ...................................................................... 39 4.3.4.  Data Analysis ......................................................................... 40 4.3.5.  Statistical Analysis ................................................................ 41 

4.4.  Results .......................................................................................... 41 4.5.  Discussion .................................................................................... 46 4.6.  Conclusion ................................................................................... 49 4.7.  Acknowledgments ........................................................................ 50 

~x~

55  A Wearable System to Assess Risk for ACL Injury During Jump Landing: Measurements of Temporal Events, Jump Height, and Sagittal Plane Kinematics ................................................................. 51 

5.1.  Overview ...................................................................................... 51 5.1.1.  List of Definitions .................................................................. 52 

5.2.  Introduction .................................................................................. 53 5.3.  Methods ........................................................................................ 54 

5.3.1.  Subjects .................................................................................. 54 5.3.2.  Experimental Design ............................................................. 54 5.3.3.  Wearable System ................................................................... 56 5.3.3.1.  Hardware ............................................................................ 56 5.3.3.2.  Angle measurements .......................................................... 56 5.3.3.3.  Temporal events detection .................................................. 57 5.3.3.4.  Vertical jump height ........................................................... 57 5.3.4.  Reference System .................................................................. 58 5.3.5.  Data Analysis ......................................................................... 59 

5.4.  Results .......................................................................................... 61 5.5.  Discussion .................................................................................... 66 5.6.  Conclusions .................................................................................. 69 5.7.  Acknowledgments ........................................................................ 69 

66  Characterization of Jump Landing Mechanics Based on Thigh and Shank Segment Angular Velocity: Implications for ACL Injury Risk ..................................................................................................... 70 

6.1.  Overview ...................................................................................... 70 6.2.  Introduction .................................................................................. 71 6.3.  Methods ........................................................................................ 72 

6.3.1.  Subjects .................................................................................. 72 6.3.2.  Experimental Design ............................................................. 73 6.3.3.  Segment angular velocity ...................................................... 74 6.3.4.  External knee moments ......................................................... 76 6.3.5.  Data Analysis ......................................................................... 76 

6.4.  Results .......................................................................................... 77 6.5.  Discussion .................................................................................... 83 6.6.  Conclusions .................................................................................. 87 6.7.  Acknowledgments ........................................................................ 87 

~xi~

77  Real Time Inertial-Based Feedback Can Reduce Risk for ACL Injury During Jump Landings ......................................................... 88 

7.1.  Overview ...................................................................................... 88 7.2.  Introduction .................................................................................. 89 7.3.  Methods ........................................................................................ 90 

7.3.1.  Subjects .................................................................................. 90 7.3.2.  Jump Task .............................................................................. 90 7.3.3.  Feedback ................................................................................ 91 7.3.3.1.  Hardware ............................................................................ 91 7.3.3.2.  Parameters .......................................................................... 91 7.3.3.3.  Relative Risk....................................................................... 92 7.3.4.  Experimental Design ............................................................. 92 7.3.4.1.  Training Session ................................................................. 95 7.3.5.  Knee Abduction Moment Measurement ............................... 96 7.3.6.  Statistical Analysis ................................................................ 97 

7.4.  Results .......................................................................................... 97 7.5.  Discussion .................................................................................. 103 7.6.  Conclusions ................................................................................ 107 7.7.  Acknowledgments ...................................................................... 107 

88  Summary .......................................................................................... 108 

8.1.  Overall Conclusions ................................................................... 108 8.2.  Contributions .............................................................................. 110 

References .............................................................................................. 112 

~xii~

List of Tables

Table 3-1: Variables of Interest at Foot Contact for Low and High Friction Surfaces 27 

Table 4-1: Variables of interest at foot contact for males and females on both low and high friction surfaces. ............................................................................................. 45 

Table 5-1: Jump height measured with wearable and reference systems. .................... 62 

Table 5-2: Similarities of the patterns (R) between the systems .................................. 64 

Table 5-3: Knee kinematic parameters measured at specific time points with both measurement systems. ............................................................................................ 65 

Table 6-1: Coefficients of multiple correlation (CMC) and ranges (SD) for the angular velocities of the shank and thigh segments in all three planes. .............................. 80 

Table 6-2: Angular velocity parameters measured at specific time points. ................. 81 

Table 6-3: Correlation (R) between knee abduction moment and coronal plane angular velocity, as well as receiver operating curves (ROC). ........................................... 82 

Table 7-1: Standardized set of movement modifications for training session ............. 95 

Table 7-2: Knee flexion angle, trunk lean, and jump height at baseline and follow-up. Number at risk indicates subjects outside the low risk ranges. .............................. 98 

Table 7-3: Thigh coronal angular velocity and knee abduction moment for both systems at baseline and follow-up. For thigh coronal angular velocity, change calculated as the average difference between baseline and follow-up (absolute value). Knee abduction moment split into at-risk (ABD Baseline) ........................ 99 

~xiii~

List of Figures

Figure 1-1: Normal knee anatomy, front view ............................................................... 2 

Figure 1-2: Arthroscopic view (left) and cadaveric dissection (right) of the anteromedial (AM) and posterolateral (PL) functional bundles of the ACL (Seibold 2008) .......................................................................................................... 3 

Figure 1-3: Scattergram of the proportion of individuals with radiographic osteoarthritis (OA) plotted against time after ACL injury or reconstructive surgery. Each data point represents a data set from 1 of 127 individual publications. Symbols: • represents nonsurgical treatment; ▾ represents primary suture or enhancement; ▪ represents reconstruction by autograft; ♦ represents reconstruction by synthetic graft or allograft (Lohmander 2007). ................................................... 4 

Figure 2-1: ACL injury through a combination of knee valgus and anterior tibial translation force during a side-cut maneuver in soccer players (Alentorn-Geli 2009a) ....................................................................................................................... 9 

Figure 2-2: Knee Abduction Moment .......................................................................... 12 

Figure 2-3: Construction of a subject’s image from a markerless motion capture system. The silhouettes of the subject from different cameras are projected into space, and their intersection forms an approximation of the volume occupied by the subject’s body (Corazza 2006) ......................................................................... 17 

Figure 2-4: Inertial sensor measurement system (Physilog®, BioAGM, CH) ............. 18 

Figure 3-1: Knee flexion angle during the entire recorded sequence. Data represent average of all trials on each surface for one subject. .............................................. 26 

Figure 3-2: External knee adduction/abduction moment during the entire recorded sequence. Data represent average of all trials on each surface for one subject. Stance phase begins at frame 0. Negative values indicate abduction moment. ..... 29 

Figure 3-3: Example measurements for relative medial and posterior center of mass (COM) distance from the support limb (defined as ankle joint center). ................ 30 

Figure 4-1: Knee flexion angle at foot contact by total and by gender. ....................... 43 

Figure 4-2: Knee moments at foot contact by total and by gender. ............................. 44 

Figure 5-1: Proposed wearable system. ........................................................................ 55 

Figure 5-2: Bland and Altman analysis of jump height. Solid line corresponds to bias and dashed lines correspond to 66% limits of agreement. ..................................... 62 

Figure 5-3: Example of continuous knee flexion angle and trunk lean for one subject during one bilateral jumping task, with the discrete time point parameters identified. ................................................................................................................ 64 

~xiv~

Figure 6-1: Experimental setup of the wearable system and the camera-based system markers. Wearable system IMUs identified with white oval. Positive axes convention for SAV identified for medial/lateral (M-L), posterior/anterior (P-A), and inferior/superior (I-S) axes. ............................................................................. 74 

Figure 6-2: Bilateral jump angular velocity curves for shank and thigh segments in sagittal, coronal, and transverse planes (axes are according to Figure 6-1). Initial contact (IC) is indicated by black circle, and maximum stance (MAX) is indicated by white star. Difference (DIF) is range between IC and MAX. ........................... 78 

Figure 6-3: Unilateral jump angular velocity curves for shank and thigh segments in sagittal, coronal, and transverse planes (axes are according to Figure 6-1). Initial contact (IC) indicated by black circle, maximum stance (MAX) indicated by white star. Difference (DIF) is range between IC and MAX. .......................................... 79 

Figure 6-4: Illustration of the relationship between coronal SAV and external knee abduction moment. A) positive thigh SAV tends to increase the knee abduction moment, B) positive shank SAV tends to decrease the knee abduction moment, C) positive difference between thigh and shank SAVs tends to increase knee abduction moment. ................................................................................................. 83 

Figure 7-1: Experimental protocol for entire testing session. ...................................... 94 

Figure 7-2: Entire testing session for one subject. For each parameter, blue circle indicates mean baseline measurements, red triangles indicate training jump values, and black X indicates mean follow-up measurements. Green shading indicates low risk range. ............................................................................................................... 96 

Figure 7-3: Change in knee flexion angle, trunk lean, and thigh coronal angular velocity by subject ................................................................................................ 100 

Figure 7-4: Change in knee abduction moment by subject from baseline to follow-up, split into at-risk and not-at-risk cohorts. At-risk cohort (top) had a positive (abduction) peak moment at baseline while not-at-risk cohort (bottom) had a negative (adduction) peak moment at baseline. ................................................... 102 

Figure 7-5: Intra-subject association between the change (baseline to follow-up) in the thigh coronal angular velocity and the knee abduction moment. ......................... 103 

~1~

11 Introduction

1.1. Overview

The overall goal of this project is to use novel motion analysis systems to

investigate the underlying mechanisms that cause an anterior cruciate ligament (ACL)

injury and then to explore movement modification methods that might prevent ACL

injuries from occurring. An ACL injury is one of the most common musculoskeletal

injuries sustained during sports participation. This injury causes immediate functional

impairment and also increases the long term risk of developing osteoarthritis (OA), a

degenerative joint disease. Thus, understanding the causes of this injury and

investigating methods to prevent it from occurring are important goals and could lead

to improved health and quality of life for recreational athletes. Additionally, novel

motion analysis systems can provide new information about ACL injuries and

therefore should be used to help analyze these injuries from a different perspective.

The remainder of this chapter provides the motivation for investigating ACL injuries,

describes the statement of purpose for this study, and gives an outline for the

following chapters.

1.2. Anterior Cruciate Ligament Injury

1.2.1. Description

The anterior cruciate ligament (ACL) is one of the four major ligaments of the

knee. On the proximal side, the ACL attaches to the posteromedial edge of the lateral

femoral condyle. It then follows an oblique course in the anteromedial direction and

distally attaches to the anterior intercondylar fossa on the tibia plateau (Bicer 2009)

(Figure 1-1). The cross-sectional area of the ACL is irregular and varies throughout

the knee; the ligament “fans out” at the tibial attachment.

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~2~

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~5~

Many injured patients elect to undergo ACL reconstruction surgery, which is

accepted as the standard of care and can successfully treat the initial loss of stability

and function (Tashman 2008). However, ACL reconstruction does not reduce the

incidence of OA (Barrack 1990; Daniel 1994; Kannus 1989; Lohmander 2004;

Lohmander 2007; Maletius 1999; Meunier 2007; von Porat 2004). Lohmander et al.

(2004) found that in a population of female soccer players who suffered ACL ruptures

at an average age of 19 years, 51% of the injured knees showed radiographic knee OA

just 12 years after injury (at age 31), compared to only 7% of the uninjured

contralateral knees. Additionally, this study showed that there was no significant

difference in the incidence of radiographic knee OA between ACL reconstructed

knees and ACL deficient knees, suggesting that the ACL reconstruction was unable to

reduce the rates of OA in this population. Another study by von Porat et al. (2004)

showed similar results in male soccer players 14 years after injury. Radiographic

changes were found in 78% of the 122 subjects studied, and advanced radiographic

changes (Kellgren-Lawrence grade 2 or higher) were observed in 41% of the subjects.

Again, there were no differences in incidence of radiographic changes between

surgically and non-surgically treated subjects, further supporting that reconstruction

does not prevent OA from occurring. All these studies suggest that ACL injury leads

to degeneration of the articular cartilage in the injured knee, and standard ACL

reconstruction procedures do not protect the knee from developing OA.

1.3. Statement of Purpose

As stated previously, ACL injury is a growing problem among athletes,

particularly among women. An injury often leads to premature degenerative arthritis,

and there is no known treatment that can reduce this increased risk. In order to reduce

the risk for an ACL injury, it is first important to understand how the injuries occur;

specifically, how subjects adapt their movement to different conditions, and how these

adaptations change their risk for ACL injury. Additionally, prevention or reduction of

the risk for ACL injuries is important for long term joint health, and therefore it is

~6~

important to determine effective methods to alter the subjects’ movements so that they

are less at risk for injury.

The underlying goal of this thesis is to fill critical gaps in the available

knowledge on the causes of ACL injury, and then to investigate methods to prevent

these injuries from occurring. Novel motion analysis systems were used for this thesis

in order to examine parameters that might affect risk for ACL injury but are difficult

to measure with standard motion analysis systems. To achieve these goals, multiple

studies were conducted of healthy subjects performing movement tasks that replicate

known ACL injury mechanisms while data was collected with two different types of

novel motion analysis systems. The causes for ACL injury were investigated,

specifically how subjects adapt their movement strategies (and therefore their risk for

injury) as a response to the coefficient of friction of the floor surface. Next, a novel,

inertial-based motion analysis system was characterized for use during jumping tasks.

This system was then used as a real time feedback system to reduce the risk for ACL

injury. The system instructed subjects how to modify their movement and then

measured how effectively the subjects were able to alter their movement technique as

well as the change in their risk for ACL injury.

1.4. Outline of Upcoming Chapters

Chapter 2 is a review of the relevant literature that pertains to understanding

and preventing ACL injuries, specifically the mechanisms of ACL injury, risk factors

for injury, and injury prevention strategies and programs. The novel motion analysis

systems used for this thesis are also discussed in this chapter.

Chapter 3 analyzes how subjects change their movement strategies for shoe-

surface conditions with a high coefficient of friction relative to a low friction condition

and how these changes in movement strategies affected their risk for ACL injury. The

study demonstrated that for the high coefficient of friction surface, the subjects

adopted a movement strategy which increased their risk for ACL injury.

Chapter 4 investigates how increasing running speed prior to a single limb

landing combined with increased floor friction alters a subject’s movement as well as

~7~

how these alterations are different between males and females. The results from this

study were that increasing the running speed on a high friction surface alters the

subjects’ risk of injury; some of the alterations are protective and some increase the

risk of injury. In terms of gender, females are more at risk for injury than males during

all the test conditions.

Chapter 5 explains the development and assessment of a wearable inertial-

based system to measure jumping tasks in terms of temporal event detection, jump

height, and knee angles. The wearable system accurately detected temporal events and

measured total jump height. It also measured the knee joint angles in all three planes

and demonstrated good concurrent validity and discriminative performance in terms of

the known risk factors for ACL injury.

Chapter 6 describes the characterization of the thigh and shank angular

velocity during a jump landing and the association between coronal angular velocity

and knee abduction moment. The coronal angular velocities were significantly

correlated with the knee abduction moment, showing that angular velocity could be a

useful parameter to analyze jump landing movements.

Chapter 7 illustrates that an independent inertial-based system can be used to

modify jump landing mechanics in order to decrease the risk for ACL injury by

providing real-time feedback based on known kinematic and kinetic injury risk

factors. This study determined that the subjects can effectively modify their jumping

technique based on feedback from the inertial system and that these movement

modifications caused a reduction in their risk for ACL injury.

Chapter 8 is a summary of the above studies (Chapter 3 though 7) and presents

the results in a unified manner. The major scientific contributions of the thesis are also

described in this chapter.

~8~

22 Review of Relevant Literature

2.1. Mechanisms of ACL Injury

The first part of this thesis focuses on understanding how ACL injuries occur;

therefore it is critical to examine the previous literature defining the main mechanisms

of non-contact ACL injuries. Qualitative analyses of ACL injuries captured on videos

taken during sports events suggest that many injuries occur at foot contact during a

landing from a jump with either one or two legs or a deceleration movement before a

change in direction (Boden 2000; Kimura 2010; Krosshaug 2007; Myklebust 1998;

Olsen 2004). Additionally, the affected knee appears to be near full extension (below

30° of flexion) at the time of injury (Boden 2000; Cochrane 2007; McNair 1990;

Olsen 2004; Teitz 2001). Boden et al. (2000) used retrospective video analysis to

define the most common kinematic positions that resulted in an ACL injury during

sports. They reported that ACL injury occurred during a deceleration movement when

the knee was close to full extension, the tibia was externally rotated, and the foot was

planted. During/after injury, a valgus collapse of the knee has been observed, most

commonly among female athletes (Boden 2000; Krosshaug 2007; Olsen 2004; Teitz

2001). Olsen et al. (2004) concluded that the ACL injury mechanism in women’s

handball was a valgus collapse combined with tibial rotation when the knee was close

to full extension. Additionally, Teitz (2001) suggested that the position of the center of

mass (COM) of the subject during injury was posterior and far from the location of the

foot-to-ground contact (support limb).

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a risk factor for injury because this signifies an overly upright posture (Blackburn

2008; Blackburn 2009; Griffin 2000). Increasing trunk flexion during landing leads to

increased hip and knee flexion angles but does not alter transverse or coronal plane

kinematics (Blackburn 2008).

In the coronal plane, many studies have suggested that an increased abduction

angle of the knee at both initial contact and maximum value during deceleration is a

risk factor for injury (Borotikar 2008; Ford 2003; Ford 2005; Ford 2006; Ford 2010;

Kanamori 2000; Pappas 2007; Russell 2006; Withrow 2006; Yu 2005). A collapse

into abduction of the lower limb is typically seen in the video evidence of actual

injuries (Boden 2000; Koga 2010; Krosshaug 2007; Olsen 2004). In a prospective

study, Hewett et al. (2005a) found that athletes that sustained an ACL injury had 8°

more knee abduction angle during landing from a jump when compared to uninjured

athletes. Ford et al. (2005) showed that females exhibited greater knee abduction

angles during cutting maneuvers than comparable male athletes. Also, knee abduction

angle has been suggested as a strong predictor of future injury (Hewett 2005a; Padua

2009a).

Finally, increased rotation of the tibia in the internal direction at initial contact

and maximum value during deceleration has been suggested to increase the risk for

ACL injury (Borotikar 2008; Kiriyama 2009; McLean 2007), and increased rotation in

both the internal and external directions has been observed during actual ACL injuries

(Koga 2010; Krosshaug 2007; Olsen 2004). Also, female athletes had an increased

knee internal rotation angle compared to male athletes during the landing preparation

of a stop-jump task (Chappell 2007).

2.2.2. Biomechanical: Kinetics

Multiple investigations of ACL injuries have suggested that specific kinetic

measures of the knee can be used to identify a higher risk for ACL injury (Alentorn-

Geli 2009a). The primary kinetic risk factor for ACL injury is the external knee

abduction moment. Biomechanical studies of both cutting tasks and jumping tasks

have indicated that subjects with an increased knee abduction moment during

~11~

deceleration have an increased risk of ACL injury (Besier 2001b; Ford 2010; McLean

2007; Renstrom 2008). In terms of gender, females typically display greater abduction

moments than men during cutting and jumping (Chappell 2002; Hewett 2006;

Renstrom 2008). Similarly, simulations of jump landings and cadaveric studies have

suggested that increased load in abduction increases the strain in the ACL (Fukuda

2003; Kanamori 2000; Markolf 1995; Shin 2009; Shin 2010; Withrow 2005). The

knee abduction moment during landing can also be used to evaluate an athlete’s risk of

injury by stratifying athletes into low-risk or high-risk categories (Myer 2007).In

terms of actual injuries, a prospective study found that female athletes that sustained

an ACL injury had a 2.5 greater peak knee abduction moment during landing than

uninjured athletes (Hewett 2005a). Furthermore, this study showed that knee

abduction moment was a stronger predictor for ACL injury than knee flexion angle.

Given this known ACL injury risk factor, it would be beneficial to have a

simple method to predict whether or not an athlete will sustain an ACL injury based

on measuring knee abduction moment during a landing after a jump. A prospective

study by Hewett et al. found that knee abduction moment during landing predicts

future ACL injury with a sensitivity of 78% and a specificity of 73% and that the

combination of knee abduction moment and knee abduction angle predicted injury

with an R2 of 0.88 (Hewett 2005a). However, measuring the knee abduction moment

during landing is a complex calculation requiring both a camera based measurement

system and a force plate to record ground reaction forces, as well as extensive time

necessary to prepare the subject for subsequent testing. Therefore, simpler methods to

predict the knee abduction moment have been investigated. Another study has

suggested that specific biomechanical parameters can predict 78% of the variance in

the knee abduction moment during landing using the peak knee abduction angle, the

peak knee flexion moment, the knee flexion angle range of motion, BMI, and length of

the tibia (Myer 2010a). These same parameters could predict a high knee abduction

moment status with 85% sensitivity and 93% specificity (Myer 2010a). Additionally, a

simpler method using clinical correlates to the previously identified laboratory

measures successfully predicted high knee abduction moment status (Myer 2010b,

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~13~

2.2.3. Environmental Factors

The primary environmental risk factor for ACL injury examined in this thesis

is the coefficient of friction of the shoe-surface interface. It has been widely

hypothesized that an increased coefficient of friction (COF) of the shoe-surface

interaction leads to increased incidence of ACL injury during sporting events

involving run-to-cut maneuvers (Alentorn-Geli 2009a; Torg 1974). This has been

suggested by studies comparing weather conditions, different types of surfaces, and

footwear. Studies have shown that weather conditions that produced dry fields are

associated with more injuries than wet fields and that most injuries occur on dry fields

(Orchard 2001; Scranton 1997). For example, 95.2% of noncontact ACL injuries

observed in the National Football League (NFL) over 5 seasons occurred on a dry

field, which has a higher COF than a wet field; similarly, weather conditions that led

to dry fields (low amounts of rainfall and high evaporation rates) had a higher relative

risk (2.87 and 2.55 greater risk, respectively) of noncontact ACL injury among

Australian football players over 7 years of play (Orchard 2001; Scranton 1997). Other

studies that examined the effect of weather on lower limb injuries in NFL games

found that there were significantly fewer knee and ankle injuries in cold weather than

warm weather, and the authors concluded that this could be a result of the reduced

shoe-surface traction in the cold climate (Orchard 2003). Further investigations on

injury rates in the NFL for artificial turf surfaces versus grass surfaces determined that

there was a higher rate of ACL injury on older versions of AstroTurf, which has a

much larger COF than natural grass (Orchard 2003; Powell 1992). Additionally, in a

video examination of ACL injury events in team handball, Olsen et al. (Olsen 2003)

determined that more ACL injuries occurred on high COF rubber floor surfaces than

wooden floor surfaces; this relationship was especially high for female athletes.

Additionally, footwear that has a higher COF has been associated with a greater risk of

ACL injury (Lambson 1996). For all these studies, the surface with the higher COF

was shown to also have a higher incidence of ACL injury.

~14~

2.3. Prevention Strategies and Programs

Recent articles have shown that the incidence of ACL injury can be decreased

among athletes through the use of intervention programs that focus on modifying

lower extremity biomechanics (Alentorn-Geli 2009b; Brophy 2010, Hewett 2006b;

Renstrom 2008; Silvers 2007). Most of these programs combine various different

intervention modifications (e.g. kinematic modifications strength training,

plyometrics, balance training, etc), and so it is unclear how each individual

modification contributes to the changes observed after the intervention and the

corresponding decrease in the incidence of injury. These intervention programs are

generally six to eight weeks in duration and require 2 to 3 training sessions per week

where the participants perform a variety of neuromuscular, plyometric, and strength

exercises. Furthermore, most of the training sessions occur during team practices

because the participants cannot perform the intervention training independently; as a

result, compliance rates for these intervention programs can be as low as 28%

(Myklebust 2003). During the training sessions, either coaches or physical therapists

must be present to provide feedback to the participants in order to ensure they are

properly performing the training intervention (Alentorn-Geli 2009b; Brophy 2010,

Hewett 2006b; Renstrom 2008; Silvers 2007). However, this feedback generally

consists of verbal instructions based on real-time visual observation; therefore, the

feedback is not quantitative in nature and can vary depending on the skill of the

observer. Additionally, not all intervention programs have proved to be successful.

2.3.1. Knee Flexion Angle Modification

Many of the successful intervention programs emphasize proper landing

technique after a landing from a jump, specifically an increase in the knee flexion

angle during landing. This focus on increasing knee flexion angle during landing

stems from previous research showing that a small knee flexion angle at both the

initial contact with the ground and the maximum angle achieved during landing is a

risk factor for ACL injury (section 2.2.1). Furthermore, investigations of actual ACL

~15~

injuries suggest that the injury occurs at a low knee flexion angle immediately

following initial contact with the ground (section 2.2.1).

Increasing the knee flexion angle during jump landing has been the primary

modification in several previous studies focused on altering lower extremity

biomechanics in order to reduce the risk for ACL injury (Herman 2009; Mizner 2008;

Myers 2010; Oñate 2005). After the intervention, the subjects were reported to have

significantly increased their knee flexion angles at both initial contact and peak value,

and also exhibited changes in other saggital plane parameters, specifically an increase

in hip flexion angle and decreases in hip flexion moment, knee flexion moment, and

anterior tibial shear force (Herman 2009; Mizner 2008; Myers 2010; Oñate 2005).

However, the correlation between the change in knee flexion angle and the change in

other risk factors has not been investigated.

2.3.2. Real-Time Feedback Modifications

Real-time training interventions have been developed primarily for repetitive

exercises such as walking or running (Barrios 2010, Crowell 2011, Dowling 2010,

Hunt 2011, Noehren 2010, Shull 2011, Wheeler 2011) and rehabilitation (Bachlin

2010, Tate 2010). In these investigations, subjects were provided with visual, auditory,

or haptic feedback that instructed them as to how to modify their movements. While

most interventional studies used real-time marker-based motion capture (Barrios 2010,

Hunt 2011, Noehren 2010, Shull 2011, Wheeler 2011), this method is limited in scope

because of the difficulties in tracking markers and processing position data in real-

time.

2.4. Novel Motion Analysis Systems

Skin marker-based motion capture, otherwise known as stereophotogrammetry,

has been widely used in studies of human movement (Andriacchi 2000), and can have

an accuracy of less than 1 mm (Chiari 2005). However, instrumental errors (Chiari

2005), soft tissue artifact (Leardini 2005), and marker misplacement (Della Croce

2005) can all affect the estimation of the skeletal system movement and are critical

~16~

sources of measurement error. Additionally, these systems require skilled operators

and complex instrumentation (e.g., multiple cameras synchronized with a force plate),

which restrict their usage for routine applications. Because of these difficulties, other

types of motion analysis systems have been proposed to study human movement. The

following sections discuss the two novel motion analysis systems used in this thesis.

2.4.1. Markerless Motion Capture

Motion capture based only on video data, known as markerless motion capture,

has become increasingly popular in the last few years because it and can be used for a

broad range of applications. Thus far, markerless motion capture has been used for

animation in the entertainment industry, sports performance evaluation, surveillance,

and biomechanical analysis for clinical applications; however, only sports

performance and biomechanical analysis require a high degree of accuracy of the

system. Additionally, markerless motion capture enables the subjects to move

naturally, minimizes the subject preparation time, and reduces inter-operator

variability since no markers are placed on the subject (Corazza 2009; Mündermann

2006). This type of motion capture is well suited to measuring movements that occur

quickly, as traditional markers have a tendency to fall off the subject during fast

movements. For example, a recent study used markerless motion capture to evaluate

different types of tennis serves in order to determine which serve places the most

stress on the body (Abrams 2011). Furthermore, markerless motion capture can be

used in situations where marker-based motion capture is impossible, such as motion

capture of animals. Due to the wide variety of animal skin, marker attachment can be

impossible or may significantly alter the animal’s natural movement, as illustrated in a

study by Zelman et al. (Zelman 2009) that used a markerless motion capture system to

track octopus arm movements in 3D space.

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~19~

33 Shoe-Surface Friction Influences Movement Strategies During a Sidestep Cutting Task: Implications for Anterior Cruciate Ligament Injury Risk

3.1. Overview Increasing the coefficient of friction of the shoe-surface interaction has been

shown to lead to increased incidence of anterior cruciate ligament (ACL) injuries, but

the causes for this increase are unknown. Previous studies indicate that specific

biomechanical measures during landing are associated with an increased risk for ACL

injury. At foot contact during a sidestep cutting task, subjects use different movement

strategies for shoe-surface conditions with a high coefficient of friction (COF) relative

to a low friction condition. Specifically, the study tested for significant differences in

knee kinematics, external knee moments, and the position of the center of mass for

different COFs. Twenty-two healthy subjects (11 male) were evaluated performing a

30° sidestep cutting task on a low friction surface (0.38) and a high friction surface

(0.87) at a constant speed. An 8-camera markerless motion capture system combined

with 2 force plates was used to measure full-body kinematics, kinetics, and center of

mass. At foot contact, subjects had a lower knee flexion angle (P = .01), lower

external knee flexion moment (P < .001), higher external knee abduction moment (P <

.001), and greater medial distance of the center of mass from the support limb (P <

~20~

.001) on the high friction surface relative to the low friction surface. The high COF

shoe-surface condition was associated with biomechanical conditions that can increase

the risk of ACL injury. The higher incidence of ACL injury observed on high friction

surfaces could be a result of these biomechanical changes. The differences in the

biomechanical variables were the result of an anticipated stimulus due to different

surface friction, with other conditions remaining constant. The risk analysis of ACL

injury should consider the biomechanical movement changes that occur for a shoe-

surface condition with high friction.

Portions of this chapter were previous published in the American Journal of

Sports Medicine in 2010 (Dowling 2010). The final, definitive version of this paper

has been published in The American Journal of Sports Medicine, 38/3, Mar/2010 by

SAGE Publications, Inc. All rights reserved. ©2010. The author contributed to this

paper by collecting all of the data from the subjects, processing the data, analyzing the

data, and writing the manuscript of the paper.

3.2. Introduction As described in Chapter 1, the ACL is frequently injured and can lead to

premature knee osteoarthritis with or without reconstruction. Qualitative analysis of

ACL injuries suggest that these injuries commonly occur at foot contact during a

landing or deceleration movement before a change in direction with the position of the

center of mass (COM) posterior and far from the location of the foot-to-ground contact

(support limb). Quantitative studies indicate that specific biomechanical measures

during landing can be used to identify an increased risk for ACL injury, specifically a

small knee flexion angle, large abduction angle or moment, and large internal or

external rotation moment (Chapter 2). One main environmental factor, an increased

coefficient of friction (COF) of the shoe-surface interaction, leads to increased

incidence of ACL injury during sporting events involving run-to-cut maneuvers

(Chapter 2). For all the studies described previously, the surface with the higher COF

was shown to also have a higher incidence of ACL injury; however, the biomechanical

~21~

changes that an athlete adopts on a high friction surface that lead to the greater

incidence of injury have not been determined.

This study tested the hypothesis that subjects use different movement strategies

for shoe-surface conditions with a high COF relative to a low friction condition at foot

contact during a sidestep cutting task. Specifically, the study tested for significant

differences in knee flexion and abduction angles, external knee moments of flexion,

abduction, and internal rotation, and the position of the COM, all between the high and

low COF conditions. These biomechanical variables were chosen to quantify the

movement strategies because all of them are associated with increased risk of ACL

injury.

3.3. Methods 3.3.1. Subjects

Twenty-two total participants volunteered for this investigation. There were 11

male and 11 female subjects with an average age of 23.6 ± 2.7 years and body mass

index (BMI) of 23.2 ± 1.9. Subjects were regular participants at the

recreational/intramural level in sports involving sidestep cutting maneuvers, as it was

assumed that these subjects would be familiar with the sidestep cutting task. Of the 22

subjects, 11 played intramural or collegiate level soccer, 5 played collegiate lacrosse,

and 6 played collegiate competitive Ultimate Frisbee. Subjects with a history of lower

limb musculoskeletal injuries requiring surgery or any current symptoms of pain or

injury were excluded from the study. Informed written consent was obtained from all

subjects before data collection and approved by the Institutional Review Board. After

the informed consent was obtained, height, mass, and age were measured and

recorded, and it was noted if the subject chose to cut from their right or left leg during

the running task (the dominant leg).

~22~

3.3.2. Experimental Design

Subjects were asked to perform a 30° sidestep cut off from their self-selected

dominant leg under 2 different surface conditions (low and high COF) in a gait

laboratory. The running task used for this study was a sidestep cut of 30° from the

direction of travel, a common task used in studies designed to assess ACL injury risk

(Besier 2001a; Besier 2001b; Cochrane 2007; Dempsey 2007; McLean 2004a;

McLean 2005; McLean 2004b; Olsen 2004). Subjects were asked to cut on both their

left and right legs during familiarization trials to determine which leg they preferred or

their dominant leg. The 30° angle was marked by tape on the floor of the laboratory

and a marker on the wall of the laboratory to give the subjects definitive points of

reference, and the subjects practiced the task until they could hit the predefined marks.

Two different surfaces were chosen for this investigation. The first surface was

a low friction surface (COF = 0.38 ± 0.03), which was achieved by placing disposable

shoe covers inside-out over the subject’s shoes and having the subject run on the high-

pressure laminate floor surface of the gait laboratory. The second surface was a high

friction surface (COF = 0.87 ± 0.19), which was achieved by taping a thick rubber mat

to the floor of the laboratory and the force plate where the subject would be running.

The rubber mat was affixed to the floor and to the force plate with tape to prevent

motion; the section placed over the force plate was separate from the floor section to

prevent transfer of force. No shoe coverings were used in the high friction trials. The

subjects were asked to wear their own comfortable athletic shoes during the test. The

COF was calculated for each individual subject’s shoe by putting the shoe in each

surface friction condition on a force plate and then conducting a horizontal pull test of

the shoe with a 25-lb weight placed on top of it. The horizontal force (F) required to

pull the shoe across the surface divided by the normal force (N) of the shoe-weight

combination was used to calculate the COF for that shoe on that surface (F = COF *

N).

Surfaces tests were randomized. The subjects were allowed to practice on each

surface before their trials were recorded. Immediately before testing, the subjects

completed a training session on the low friction surface. During this session, the

~23~

subjects were asked to perform the cutting task several times to find the fastest

possible comfortable speed in the low friction condition. This self-selected running

speed was then chosen as the standard running speed for all surface conditions. This

protocol ensured the safety of the subjects during the low friction trials. The subjects

then completed 5 acceptable trials of the running task on each surface and were given

a 1-minute interval of rest between each trial to prevent fatigue. A trial was considered

acceptable if the subject completed the task within 0.2 m/s of the standard running

speed, achieved approximately a 30° angle during the cut (±5° by visual inspection),

and was fully recorded by the data collection system.

3.3.3. Data Collection

A markerless motion capture (MMC) system combined with 2 force plates was

used to collect full body kinematics and lower limb kinetics; the MMC system was

chosen because it does not require placing markers/fixtures on the body that could

affect the natural motion of the subject (Corazza 2009; Mündermann 2006). Video

recordings of the subject trials were captured at a frequency of 120 Hz by 8 VGA

color cameras, resolution 640 by 480 pixels (Allied Vision Technologies, Stadtroda,

Germany), and a multiple video stream acquisition system (Simi Motion Analysis,

Unterschleissheim, Germany). A 3-dimensional representation of the subject, or visual

hull, was created using a previously described volume intersection method at every

frame (Mündermann 2005). A full-body laser scan (Cyberware, Monterey, California)

provided an accurate description of the subject’s outer body surface and was used to

create a subject-specific model. The body scan was automatically divided into 15 rigid

segments with 6 degrees of freedom between adjacent segments, and the joint centers

between these body segments were identified (Corazza 2009). This model was then

matched to the visual hulls throughout the entire recorded sequence and used to extract

the locations of the joint centers of the subject using a previously described matching

process (Corazza 2006). Ground-reaction forces and moments were collected using 2

multi-component force plates (Bertec, Columbus, Ohio) recording at 120 Hz and

synchronized with the video camera system.

~24~

3.3.4. Data Analysis

Once the joint centers for the entire sequence were identified, the kinematic

and kinetic calculations were completed based on previously described methods

(Andriacchi 2003; Andriacchi 2004). Knee rotations were expressed as the angles

between 2 vectors, created along the long axes of the shank and thigh segments,

projected onto the global reference planes (Andriacchi 2003). This method of angle

calculation was validated against marker-based motion capture data as accurate at the

instant of foot contact during stance (Andriacchi 2003). To calculate external moments

at each joint center, each lower limb segment (foot, shank, thigh) was idealized to be a

rigid body. The foot was assumed to be massless, and the shank and thigh segment

inertial properties were taken from the literature (Dempster 1967). External

intersegmental moments for each trial were calculated from the joint center locations

from the MMC system, force plate data, and inertial segment data using an inverse

dynamics approach (Andriacchi 2004). Moments were normalized to body weight and

height (%Bw*Ht) to allow for comparison between subjects. Last, the COM of the

subject was approximated, assuming homogeneous density of the body, by measuring

the center of volume of the visual hull for every frame of the recorded trial and then

assuming this location as the COM. The difference between the global position of the

COM and the global position of the ankle joint center was calculated (normalized to

height, %Ht) for each frame in the sagittal and coronal planes to give a relative

measure of distance of the COM that could be compared between subjects.

The stance phase of the sidestep cut was defined as the interval when the

ground-reaction force was greater than 10 N. The kinetic measurements were

calculated during the weight acceptance phase of stance, defined as the phase from

foot contact until the first trough in the total ground-reaction force (vector summation

of Fx, Fy, and Fz) (Besier 2001b). These measurements were used because previous

work has suggested that during a single-limb landing task, the strain in the ACL

reaches a maximum value at the beginning of stance (Cerulli 2003). The kinematic

and COM measurements were calculated during the final 20% of the flight phase

~25~

preceding foot contact plus the weight acceptance phase; this was done to accurately

record peaks and troughs in the data that sometimes occurred slightly before foot

contact. Minimum knee flexion angle, maximum knee flexion moment, minimum

posterior COM, and maximum medial COM were measured. Maxima and minima,

rather than means, were measured for these variables because definitive peaks and

troughs were observed, and these extremes could represent dangerous loading

patterns. For the remaining variables, the data were averaged across the phase of

interest: weight acceptance phase for the kinetic measurements, and final 20% of

stance plus weight acceptance phase for kinematic and COM measurements. For each

biomechanical variable, one datum point per subject was calculated by measuring all 5

recorded trials and averaging these 5 values to determine the subject’s overall

performance during the testing. The approach running speed of the subject was

determined by calculating the horizontal distance traveled by the joint center of the

abdomen before initial foot contact divided by the amount of time to traverse this

distance. The final cutting angle was calculated by determining the anterior/posterior

and medial/lateral displacement of the abdomen joint center for each time point from

toe-off until the end of the recorded trial and then averaging the calculated angle

created by these displacements from the approach axis (Besier 2001b).

3.3.5. Statistical Analysis

The data for this statistical analysis were the knee flexion and abduction

angles, the 3 external knee joint moments (flexion, abduction, internal rotation), and

the relative position of the COM in the medial and lateral directions, all at foot

contact. Paired 2-tailed Student t tests with friction surface as the intertest factor were

used to detect significant differences between the 2 surface friction conditions in the

variables stated above. All statistical tests were performed in MATLAB version

R2007b (Mathworks, Natick, Massachusetts), and the significance level was set a

priori to α = .05 with a Bonferroni correction for multiple comparisons.

3.4. Re

The

between mo

was signific

trials condu

(Table 3-1,

Figure r

esults high and low

ovement stra

cantly less du

ucted on the

Figure 3-1).

3-1: Knee frepresent av

w COF cond

ategies durin

uring the fin

e high frictio

flexion anglverage of all

~26~

ditions were

ng the run-to

nal 20% of s

on condition

le during thl trials on ea

~

e associated

-cut trials. T

stance plus w

n relative to

he entire recach surface

with signific

The peak kne

weight accep

o the low fri

corded sequfor one sub

cant differen

ee flexion an

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iction condi

ence. Data bject.

nces

ngle

e for

tion

~27~

Kinetic, Kinematic, or COM Variable

Friction Surface

Significant Difference

Low Friction High Friction

Mean SD Mean SD

Knee Flexion Angle (°) 23.38 7.6 20.60 8.3 *(p < 0.01) Knee Abduction Angle (°) 6.93 3.3 6.52 3.8 Knee Flexion Moment (%BW*Ht) 5.80 2.4 3.39 1.6 *(p < 0.001) Knee Adduction/Abduction Moment (ADDUCTION+) (%BW*Ht) 1.10 1.1 -0.10 1.8 *(p < 0.001) Knee Internal Rotation Moment (%BW*Ht) 0.50 0.4 0.53 0.5 Medial Distance COM (%Ht) 9.18 2.0 10.42 2.0 *(p < 0.001) Posterior Distance COM (%Ht) 17.70 3.0 18.14 4.0 Speed (m/s) 3.17 0.4 3.23 0.5 Cutting Angle (°) 24.09 4.4 29.35 4.0 *(p < 0.001)

Table 3-1: Variables of Interest at Foot Contact for Low and High Friction Surfaces

~28~

The average difference in knee flexion angle for the total population was a

decrease of 2.8° on the high friction condition. The individual subject response to the

change from the low to high condition ranged from a 5° increase in knee flexion angle to

a 10.5° decrease in knee flexion angle. Five subjects had a greater knee flexion angle on

the high friction surface. The trials conducted on the high friction condition were

associated with a significantly lower peak knee flexion moment during the weight

acceptance phase (Table 3-1). The average difference for the flexion moment was a

decrease of 2.4 %BW*Ht on the high friction condition. The individual subject response

to the change from the low to high condition ranged from a 3.0 %BW*Ht increase in

knee flexion moment to a 7.4 %BW*Ht decrease in knee flexion moment. Two subjects

had a greater knee flexion moment on the high friction surface. Additionally, the average

knee abduction moment was significantly higher for the high friction condition during the

weight acceptance phase; on the low friction condition, there was an average adduction

moment, while on the high friction condition, there was an average abduction moment.

The average difference in the abduction moment was an increase of 1.2 %BW*Ht on the

high friction surface. The individual subject response to the change from the low to high

condition ranged from a 4.5 %BW*Ht increase in knee abduction moment to a 2.5

%BW*Ht decrease in knee abduction moment. Two subjects had a lower knee abduction

moment on the high friction surface (Table 3-1, Figure 3-2).

Fse

m

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Figure 3-2: equence. Da

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24° for the lo

~29~

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Figure 3-3: E(COM

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~30~

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Nair 1990; O

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ewett

ments

uction

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nt was

~31~

a stronger predictor for ACL injury than knee flexion angle. While an increase of 1.2

%BW*Ht is smaller than previously reported values, it is clinically important because 20

of the 22 subjects responded in similar fashion, suggesting that the increase in the

abduction moment associated with the increased COF represents a strong trend that

would definitely change the risk of injury. Considering the previous studies concluding

that a higher abduction moment presents a risk factor for ACL injury, the results of this

study suggest that the increase in the abduction moment on the high friction surface could

help to explain the greater incidence of ACL injury observed in conditions that result in

high shoe-surface friction.

Some subjects exhibited different biomechanical changes at the knee from the

presented results. For knee flexion angle, 5 subjects were opposite the trend; for knee

flexion and abduction moments, 2 subjects were opposite the trend, and for medial

distance of the COM, 3 subjects were opposite the trend. These were not the same

subjects, as only 2 subjects exhibited opposite reactions in 2 or more biomechanical

variables. The differences in these subjects were not correlated with any of the other

variables measured during the study. Determining why certain subjects responded in a

different manner to the same change in stimulus would improve the clinical

understanding of movement adaptation and injury risk, but the cohort of opposite

responders in this study was too small to draw any significant or relevant conclusions.

However, because ACL injury is still a relatively rare occurrence among athletes, the

changes exhibited by the opposite responders may be indicative of why certain athletes

will eventually tear their ACL and others will not.

Overall, the results of this study suggest that subjects change their landing

mechanics before foot contact as a result of the anticipation of the change in COF. All the

biomechanical variables were evaluated at foot contact because this is the instant during

the stance phase where most ACL injuries occur, as previously reported. Evaluating the

subject at foot contact allowed us to determine the subject’s anticipatory movements that

position the limb at landing. The differences in the biomechanical variables were the

result of an anticipated stimulus due to different surface friction, with other conditions

remaining constant. Given the same initial conditions of running speed, running distance,

~32~

and so on, anticipation of the change in the COF of the surface was the only stimulus

necessary to affect a significant change in the subjects’ movement strategies.

This study showed that changing the COF of the cutting surface resulted in a

change in the subjects’ movement strategies based on the anticipation of the COF and

that the strategy adopted for the higher COF surface could increase the risk of ACL

injury. The observation that subjects can modify their movement strategies in anticipation

of the landing surface friction has several important implications when considering

methods to prevent or reduce the risk of ACL injury. The subjects required minimal

exposure to a different COF condition to adapt their patterns of movement and produce

the anticipatory changes observed during the run-to-cut task. These findings suggest that

different training mechanisms could be quickly adopted by an athlete to lower the risk of

injury on a high COF surface.

While it might be difficult to train an athlete to control a quantity such as the

abduction moment at the knee, the position of the COM might be a more feasible target

for developing training strategies to prevent injury. It has been shown that poor

neuromuscular control of the trunk during a sudden force-release task predicts knee

injury risk (Hewett 2005b; Zazulak 2007), and given the mass of the torso, poor

neuromuscular control could manifest itself in altered COM position. Our study showed

that the subjects adopted a more medial position of the COM on the higher COF

condition; it has been previously reported that during actual ACL injury events, the

position of the COM of the subject was posterior and farther from the location of the

support limb (Teitz 2001). Subtle arm movements have also been observed to influence

knee abduction moments during a run-to-cut movement, which could be indicative of a

change in the position of the COM (Chaudhari 2005). Thus, training programs focused on

positioning the COM could help an athlete run on a high COF surface with the movement

strategy and protective adaptations associated with a low COF surface. Anterior cruciate

ligament injury prevention programs focused on neuro-muscular training and control

have been successfully implemented (Gilchrist 2008; Mandelbaum 2005), suggesting that

a neuromuscular training program specifically focused on COM positioning could result

in a much lower risk of injury on high friction surfaces.

~33~

One consideration in evaluating the results of this study is that only 2 different

surface COF conditions were tested, while athletes in playing situations can experience a

much broader range of friction conditions. Additionally, the high COF surface was not as

high as some previously reported values for artificial playing surfaces (McNitt 2008).

Further, the subjects were studied in a controlled laboratory environment moving at much

slower speeds than typically observed during competitive sports play, and the sidestep

cutting task was not as taxing as the typical deceleration and pivot maneuvers seen during

athletic competitions. Even with the limitations, the statistically significant effects of

surface friction on knee kinematics, kinetics, and COM observed in this study should be

considered as potential contributing factors to the increased incidence of ACL injury in

high friction conditions.

The suggestion that the biomechanical changes observed on the high COF

condition are associated with an increased risk for ACL injury is supported by studies

describing the frequency of ACL injury on various surface conditions. For example,

95.2% of noncontact ACL injuries observed in the National Football League (NFL) over

5 seasons occurred on a dry field, which has a higher COF than a wet field; similarly,

weather conditions that led to dry fields (low amounts of rainfall and high evaporation

rates) had a higher relative risk (2.87 and 2.55 greater risk, respectively) of noncontact

ACL injury among Australian football players over 7 years of play (Orchard 2001;

Scranton 1997). Other studies that examined the effect of weather on lower limb injuries

in NFL games found that there were significantly fewer knee and ankle injuries in cold

weather than warm weather, and the authors concluded that this could be a result of the

reduced shoe-surface traction in the cold climate (Orchard 2003). Further investigations

on injury rates in the NFL for artificial turf surfaces versus grass surfaces determined that

there was a higher rate of ACL injury on older versions of AstroTurf, which has a much

larger COF than natural grass (Orchard 2003; Powell 1992). Additionally, in a video

examination of ACL injury events in team handball, Olsen et al. (Olsen 2004) determined

that more ACL injuries occurred on high COF rubber floor surfaces than wooden floor

surfaces. Thus, reducing the COFs of these artificial surfaces may result in a significant

reduction in ACL injuries. The results of this study suggest that an analysis of the

~34~

movement strategies implemented on different COF conditions could help to determine

the potential risk for ACL injury of these various shoe-surface conditions.

3.6. Conclusion Taken together with existing literature, this study supports the hypothesis that

increasing the COF of the shoe-surface condition will change a subject’s movement

strategies during a sidestep cutting task in specific ways that may increase the risk of

ACL injury, providing a biomechanical basis for the increased incidence of ACL injuries

on high friction surfaces. This study found that a high COF condition was associated with

a lower knee flexion angle, higher external knee flexion and knee abduction moments,

and greater medial distance of the COM from the support limb, all of which suggest an

increased risk for ACL injury. Therefore, the higher incidence of ACL injury observed on

high friction conditions could be a result of these biomechanical changes. The subjects

exhibited the ability to adapt quickly to surface conditions after minimal training,

suggesting that focused training mechanisms could be developed to help lower the risk of

injury on high COF surfaces. This study provided additional insight into the influence of

shoe-surface friction on the risk for ACL injury.

3.7. Acknowledgments The authors thank the volunteer subjects for their participation. Special thanks to

students Erica Holland, Nathan Fenner, Katerina Blazek, and Jenssy Rojina for their

assistance in collecting and processing the data. This research was supported by the

National Science Foundation grant #03225715.

~35~

44 Running Speed and Gender Influence Movement Strategies During a Sidestep Cutting Task on Different Friction Surfaces: Implications for ACL Injury Risk

4.1. Overview Female athletes are 4-6 times more likely to suffer an anterior cruciate

ligament (ACL) injury than their male counterparts in the same landing and cutting

sports. Also, increasing the coefficient of friction of the shoe-surface interaction has

been shown to lead to increased incidence of ACL injuries. This study tested the

hypotheses that increasing running speed prior to a single limb landing combined with

increased floor friction would alter a subject’s movement and these changes will be

altered more in females than males. Twenty-two healthy subjects (11 male) were

evaluated performing a 30° sidestep cutting task under three different conditions; on a

low friction surface at low speed, on a high friction surface at the same low speed, and

on the same high friction surface at a high speed. An 8-camera markerless motion

capture system combined with 2 force plates was used to measure full-body

kinematics, kinetics, and center of mass. The biomechanical changes associated with a

greater running speed on a high friction surface were increased knee flexion angle

(increase of 6°), increased knee flexion, adduction, and internal rotation moments

~36~

(between 0.4 %BW*Ht and 1.1 %BW*Ht), and a greater medial and posterior distance

(approximately 4 %Ht) of the center of mass from the support limb. For every

condition females exhibited significantly lower knee flexion angles (approximately 6°

lower) than their male counterparts and showed a trend towards an increased knee

abduction angle (approximately 3° greater). Increasing the running speed on a high

friction surface prior to a single limb landing alters movement in the biomechanical

variables associated with ACL injury risk. Some of these alterations suggest that that

the subjects are adopting protective mechanisms to reduce their risk of injury during

this condition while other alterations suggest that the subjects are increasing their risk.

Furthermore, the differing adaptations to the high friction surface observed at different

speeds suggest that the biomechanical causes for the higher incidence of ACL injury

on high friction surfaces change based on the speed of the maneuver. In terms of

gender, the differences in the movement strategies between females and males suggest

that women are more at risk for ACL injury during all three trial conditions.

4.2. Introduction As described in Chapter 1, the ACL is frequently injured and can lead to

premature knee osteoarthritis with or without reconstruction. Previous research has

shown that gender is a risk factor for ACL injury as female athletes are 4-6 times more

likely to suffer an ACL injury than their male counterparts in the same landing and

cutting sports. Qualitative analysis of ACL injuries suggest that these injuries

commonly occur at foot contact during a landing or deceleration movement before a

change in direction with the position of the center of mass (COM) posterior and far

from the location of the foot-to-ground contact (support limb). Quantitative studies

indicate that biomechanical measures during landing can be used to identify an

increased risk for ACL injury, specifically a small knee flexion angle, large abduction

angle or moment, and large internal or external rotation moment (Chapter 2).

One main extrinsic factor, an increased coefficient of friction (COF) of the

shoe-surface interaction, leads to increased incidence of ACL injury during sporting

events involving run-to-cut maneuvers especially among females (Chapter 2). For all

~37~

the studies described previously, the surface with the higher COF was shown to also

have a higher incidence of ACL injury. Another extrinsic factor affected ACL injury

risk is speed of movement. The influence of speed during a run to cut maneuver has

not been fully investigated as a possible risk factor for ACL injury. Previous literature

has indicated that running speed is not correlated with increased incidence of injury

(Cochrane 2007), as ACL injuries were observed at speeds ranging from a slow

jogging to sprinting. However, other studies have suggested that higher speed

movements result in more ACL injuries (Myklebust 1998; Pope 2002).

Previous research on the influence of the shoe surface friction on movement

strategies during a run to cut maneuver suggested that a high COF condition was

associated with a lower knee flexion angle, higher external knee flexion and knee

abduction moment, and greater medial distance of the center of mass from the support

limb, all of which suggest an increased risk for ACL injury and might be the cause of

the higher incidence of ACL injury on high friction surfaces (Dowling 2010).

However, this study focused on subjects cutting at low speed, and did not differentiate

the results by gender. Thus gender, landing biomechanics, and friction are all potential

risk factors for ACL injury, but the interaction of these risk factors in the context of

running speed has not yet been addressed. As such, this study tested the hypotheses

that increasing running speed prior to a single limb landing combined with increased

floor friction would alter a subject’s movement, specifically in the knee flexion and

abduction angles, external knee moments of flexion, abduction, and internal rotation,

and the position of the center of mass, and these changes will be altered more in

females than males.

4.3. Methods 4.3.1. Subjects

Twenty-two total participants volunteered for this investigation (Dowling

2010). There were 11 male and 11 female subjects with an average age of 23.6 ± 2.7

years and BMI of 23.2 ± 1.9. Subjects were regular participants at the

recreational/intramural level in sports involving sidestep cutting maneuvers. Subjects

~38~

with a history of lower limb musculoskeletal injuries requiring surgery or any current

symptoms of pain or injury were excluded from the study. Informed written consent

was obtained from all subjects prior to data collection and approved by the

Institutional Review Board. After the informed consent was obtained, height, mass,

and age were measured and recorded, and it was noted whether the subject chose to

cut off their right or left leg during the running task (the dominant leg).

4.3.2. Experimental Design

Subjects were asked to perform a 30° sidestep cut off of their self-selected

dominant leg in a gait laboratory under three different conditions; on a low friction

surface at low speed, on a high friction surface at the same low speed, and on the same

high friction surface at a high speed. The running task used for this study was a

sidestep cut of 30° from the direction of travel, a common task used in studies

designed to assess ACL injury risk (Besier 2001a; Besier 2001b; Cochrane 2007;

Dempsey 2007; McLean 2004; McLean 2005). Subjects were asked to cut on both

their left and right legs to determine their dominant leg. The 30° angle was marked in

the lab, and the subjects practiced the task until they could hit the predefined marks.

Two different surfaces were chosen for this investigation. The first surface was a low

friction surface (COF = 0.38 ± 0.03) which was achieved by placing disposable shoe

covers inside-out over the subject’s shoes. The second surface was a high friction

surface (COF = 0.87 ± 0.19) which was achieved by taping a thick rubber mat to the

floor of the laboratory and the force plate where the subject would be running. The

subjects were asked to wear their own comfortable athletic shoes during the test. The

coefficient of friction was calculated for each individual subject’s shoe by putting the

shoe in each surface friction condition on a force plate and then conducting a

horizontal pull test of the shoe with a 25 lb weight placed on top of it. Surfaces tests

were randomized and the subjects were allowed to practice on each surface before

their trials were recorded.

To determine speeds, the subjects completed a training session on the low

friction surface. During this session, the subjects were asked to perform the cutting

~39~

task several times to find the fastest possible comfortable speed in the low-friction

condition, which was then chosen as the standard running speed the low speed trials.

This protocol ensured the safety of the subjects during the low friction trials. For the

high speed trials, subjects were ask to perform the cutting task at the fastest possible

speed that they could attain within the confines of the lab, and then asked to maintain

this speed during all subsequent high speed trials. The subjects completed five

acceptable trials of the running task for each condition, and were given a one-minute

interval of rest between each trial to prevent fatigue. A trial was considered acceptable

if the subject completed the task within 0.2 meters/second of the chosen running

speed, achieved approximately a 30° angle during the cut (± 5° by visual inspection),

and was fully recorded by the data collection system.

4.3.3. Data Collection

A markerless motion capture (MMC) system combined with two force plates

was used to collect full body kinematics and lower limb kinetics; the MMC system

was chosen because it does not require placing markers/fixtures on the body that could

affect the natural motion of the subject (Corazza 2006; Mündermann 2006). Video

recordings of the subject trials were captured at a frequency of 120 Hz by eight VGA

color cameras, resolution 640 by 480 pixels (Allied Vision Technologies, Stadtroda,

Germany), and a multiple video stream acquisition system (Simi Motion Analysis,

Unterschleissheim, Germany). A 3D representation of the subject, or visual hull, was

created using a previously described volume intersection method at every frame

(Mündermann 2005). A full-body laser scan (Cyberware, Monterey, CA) was used to

create a subject-specific model with the joint centers between these body segments

identified (Corazza 2009). This model was then matched to the visual hulls and used

to extract the locations of the joint centers of the subject using a previously described

matching process (Corazza 2006). Ground reaction forces and moments were collected

using two multi-component force plates (Bertec, Columbus, OH) recording at 120 Hz

and synchronized with the video camera system.

~40~

4.3.4. Data Analysis

Once the joint centers for the entire sequence were identified, the kinematic

and kinetic calculations were completed based on previously described methods

(Andriacchi 2003; Andriacchi 2004; Dowling 2010). Knee rotations were expressed as

the angles between two vectors, created along the long axes of the shank and thigh

segments, projected onto the global reference planes (Andriacchi 2003). To calculate

external moments at each joint center, each lower limb segment (foot, shank, thigh)

was idealized to be a rigid body. The foot was assumed to be massless, and the shank

and thigh segment inertial properties were taken from the literature (Dempster 1967).

External intersegmental moments for each trial were calculated from the joint center

locations from the MMC system, force plate data, and inertial segment data using an

inverse dynamics approach (Andriacchi 2004). Moments were normalized to

bodyweight and height (%Bw*Ht) to allow for comparison between subjects. Last, the

center of mass (COM) of the subject was approximated by measuring the center of

volume of the visual hull then assuming this location as the center of mass. The

difference between the global position of the COM and the global position of the ankle

joint center was calculated (normalized to height, %Ht) in the sagittal and coronal

planes to give a relative measure of distance of the center of mass that could be

compared between subjects.

The stance phase of the sidestep cut was defined as the interval when the

ground reaction force was greater than 10N. The kinetic measurements were

calculated during the weight acceptance phase of stance, defined as the phase from

foot contact until the first trough in the total ground reaction force (Besier 2001b),

because this is when the strain in the ACL reaches a maximum value at the beginning

of stance (Cerulli 2003).The kinematic and center of mass measurements were

calculated during the final 20% of the flight phase preceding foot contact plus the

weight acceptance phase. Minimum knee flexion angle, maximum knee flexion

moment, minimum posterior COM, and maximum medial COM were measured. For

the remaining variables, the data were averaged across the phase of interest: weight

acceptance phase for the kinetic measurements, and final 20% of stance plus weight

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acceptance phase for kinematic and COM measurements. For each biomechanical

variable, one datum point per subject was calculated by measuring all five recorded

trials and averaging these five values to determine the subject’s overall performance

during the testing. The approach running speed of the subject was determined by

calculating the horizontal distance traveled by the joint center of the abdomen before

initial foot contact divided by the amount of time to traverse this distance. The final

cutting angle was calculated by determining the anterior/posterior and medial/lateral

displacement of the abdomen joint center for each time point from toe-off until the end

of the recorded trial, and then averaging the calculated angle created by these

displacements from the approach axis (Besier 2001b).

4.3.5. Statistical Analysis

The data for this statistical analysis were the knee flexion and abduction

angles, the three external knee joint moments (flexion, abduction, internal rotation)

and the relative position of the center of mass in the medial and lateral directions, all at

foot contact. A mixed-model ANOVA was used to detect significant differences

between gender and the three conditions for the variables stated above. For the

statistical analysis, gender was the between-subjects independent variable while

condition type was the within-subjects independent variable. All statistical tests were

performed in MATLAB version R2007b (The Mathworks, Natick, MA), and the

significance level was set a priori to α = 0.05 with a Bonferroni correction for

multiple comparisons.

4.4. Results Increased running speed and gender were associated with significant

differences in movement during the run to cut trials. There were significant differences

in all three conditions and between males and female for the peak knee flexion angle

during the final 20% of stance plus weight acceptance phase. Both genders exhibited a

lower knee flexion angle on the high friction surface relative to the low friction

surface (decrease of 3°) for the low speed trials, and increasing the speed resulted in a

~42~

greater knee flexion angle (increase of 6°) on the high friction surface (Table 4-1,

Figure 4-1). There were statistically significant differences between the three test

conditions (p < 0.001). In terms of gender, females displayed the described pattern of

movement changes, but for every condition females exhibited significantly lower knee

flexion angles than their male counterparts (Table 4-1). Females had about 5° lower

knee flexion during the low friction, low speed condition, 6° lower knee flexion on the

high friction, low speed condition, and 6° lower knee flexion on the high friction, high

speed condition (Table 4-1).

Additionally, the knee abduction angle was not significantly different between

the three conditions, but did show a trend towards significance between genders.

Females displayed approximately 3° greater knee abduction angle on all three

conditions compare to males, but the results were not statistically significant (p = 0.1)

(Table 4-1).

The three knee moments (flexion, abduction, internal) were all significantly

different for the three test conditions during the weight acceptance phase (Table 4-1,

Figure 4-2) but not between genders. Both genders exhibited a lower knee flexion

moment on the high friction surface relative to the low friction surface (decrease of 2.4

%BW*Ht) for the low speed trials, and increasing the speed resulted in a greater knee

flexion moment (increase of 1.1 %BW*Ht) on the high friction surface (Table 4-1).

However, the greatest knee flexion moment was measured during the low friction, low

speed condition (Table 4-1). Both genders exhibited an abduction moment on the high

friction surface and an adduction moment on the low friction surface (change of 1.2

%BW*Ht) for the low speed trials, and increasing the speed resulted in a adduction

knee moment (change of 0.4 %BW*Ht) on the high speed, high friction condition

(Table 4-1, Figure 4-2). However, the adduction knee flexion moment during the low

friction, low speed condition was larger in magnitude than during the high friction,

high speed condition (Table 4-1). Last, males and females displayed no difference in

the internal rotation moment at the knee between the low and high friction surfaces,

but increasing the speed resulted in a greater internal rotation moment on the high

friction surface for both genders (increase of 0.4 %BW*Ht) (Table 4-1, Figure 4-2).

Figu

re 4-1: Kne *

** =

e flexion an= differencdifference i

~43~

ngle at foot cce in all condin males and

~

contact by tditions (p <d females (p

total and by< 0.01) p < 0.01)

y gender.

Fig

~

gure 4-2: Kn^ = dif

= difference# = dif

nee momentfference in He in Low COfference in H

~44~

ts at foot coHigh COF, OF and HigHigh COF, H

~

ontact by totLow Speed

gh COF, lowHigh Speed

tal and by g(p < 0.01)

w speed (p <d (p < 0.01)

gender.

< 0.01)

~45~

Knee Kinetic, Kinematic,

or COM Variable

Condition Surface

Low Friction, Low Speed High Friction, Low Speed High Friction, High Speed

P-Value Male Female Male Female Male Female

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

Flex Angle (°) 25.89 9.59 20.87 4.00 23.89 9.24 17.32 5.96 29.29 8.83 23.07 6.13 ^^p < 0.05

*p < 0.001

Abd Angle (°) 6.53 3.65 7.02 2.94 5.66 3.17 6.43 3.17 7.45 6.48 5.93 1.99 ^p = 0.1

Flex Mom (%BW*Ht)

6.22 2.23

5.45 2.65

3.25 1.49 3.51 1.81

4.88 1.68 4.30 2.25 *p < 0.001

Add/Abd Mom (ADD+) (%BW*Ht)

1.24 1.08

0.98 1.14

-0.25 2.08 0.03 1.60

0.93 2.29 0.57 2.55 *p < 0.01

Int Mom (%BW*Ht)

0.55 0.30

0.47 0.47

0.55 0.57 0.51 0.47

1.01 0.42 0.82 0.69 *p < 0.001

Medial COM (%Ht)

9.42 2.32

8.95 1.77

10.60 1.60 10.25 2.39

11.99 1.74 10.69 4.37 *p < 0.001

Posterior COM (%Ht)

17.91 3.45

17.48 2.59

17.38 3.51 18.89 4.51

19.56 3.46 24.40 14.81 *p < 0.05

Speed (m/s) 3.30 0.39 3.04 0.31 3.35 0.54 3.12 0.38 3.92 0.49 3.80 0.45 *p < 0.001Cutting Angle (°)

24.13 5.17

24.05 3.76

27.70 3.43 31.01 3.98

28.66 8.28 27.79 6.39 *p < 0.001

Table 4-1: Variables of interest at foot contact for males and females on both low and high friction surfaces. ^^Significant differences between genders; ^Trend to significant differences between genders;

*Significant differences between three trial conditions for all subjects.

~46~

The location of the center of mass (COM) was positioned at a significantly greater

distance in the medial direction for each successive test condition during the final 20% of

stance plus weight acceptance phase for all the subjects. Both genders exhibited greater

distance in the medial direction on the high friction surface relative to the low friction

surface (increase of 1.2 %Ht) for the low speed trials, and increasing the speed resulted in

a greater distance in the position of the COM (increase of 1.0 %Ht) on the high friction

surface (Table 4-1). The location of the center of mass (COM) was positioned at a

significantly greater distance in the posterior direction for the high speed, high friction

condition during the final 20% of stance plus weight acceptance phase for all the

subjects. Both genders exhibited greater distance in the posterior direction in the high

friction, high speed condition (increase of 4.0 %Ht) relative to the other two conditions

(Table 4-1).

The difference in speed was not statistically significant between males and

females during any test condition (Table 4-1), but was significantly greater during the

high speed trials (3.85 m/s) compared to the low speed trials (3.20 m/s). The cutting angle

of the subject was significantly different between the low and high friction conditions but

not between gender or at higher speed (Table 4-1). During the running task, the subjects

were able to obtain the desired cutting angle of approximately 30° off the vertical axis on

the high friction surface at both low and high speeds, but were only able to obtain an

average cutting angle of 24° for the low friction condition.

4.5. Discussion This study supported the hypothesis that increasing the running speed on a high

friction surface prior to a single limb landing alters movement in the biomechanical

variables associated with ACL injury risk, and that these changes affect females more

than males. The biomechanical changes associated with a greater running speed on a high

friction surface were increased knee flexion angle, increased knee flexion, adduction, and

internal rotation moments, and a greater medial and posterior distance of the center of

mass from the support limb. Additionally, females exhibited a decreased knee flexion

angle and a trend towards an increased knee abduction angle for all three conditions when

compared to males.

~47~

Some of the adaptations that the subjects exhibited on the high friction surface at

high speed suggest that that they adopt protective mechanisms to reduce the risk of ACL

injury during this condition. In terms of kinematic variables, a decreased knee flexion

angle between 0° and 30° has been suggested as increasing the risk of ACL injury (Boden

2000; McNair 1990; Olsen 2004; Teitz 2001), and strain in the ACL increases with

decreased knee flexion angle when combined with abduction or internal-rotation loading

(Hame 2002; Markolf 1995). The subjects displayed the largest knee flexion angle at foot

contact for the high speed, high friction condition, suggesting that the subjects reduce the

risk of injury at high speed. Additionally, the subjects exhibited a adduction moment

during the high friction, high speed condition, which could also be a protection

mechanism; numerous studies have identified an increased knee abduction moment as

increasing the risk for ACL injury, through biomechanical analysis, video evidence,

simulations, and cadaveric studies (Ford 2005; Fukuda 2003; Hewett 2005a; Kanamori

2000; Lloyd 2001; Markolf 1995; McLean 2005; Shin 2008; Shin 2007).

However, the increase in external knee moments and change in position of the

COM suggest that subjects also increase the risk for ACL injury during the high friction,

high speed condition. Previous cadaveric studies have suggested that the addition of a

significant internal rotation moment can drastically increase the strain in the ACL and

therefore increase the risk of ACL injury (Markolf 1995; Kanamori 2000). Markolf et al.

determined that a combined loading state of the knee that included an internal rotation

torque was an important loading mechanism of the ACL when the knee was in an

extended position (Markolf 1995). Specifically, this study determined that a adduction

moment of the knee at less than 30° of knee flexion significantly increased the loading in

the ACL, and an internal torque moment increased the loading in the ACL at all flexion

angles (Markolf 1995). Additionally, the external knee flexion moment was also higher

during the high friction, high speed condition when compared to the high friction, low

speed condition; this is significant as an increase in the flexion moment increased the

overall loading of the knee and can contribute to the total loading of the ACL (Markolf

1995). Furthermore, the location of the center of mass was significantly different for all

three surface conditions, and increasing the speed of the maneuver resulted in a greater

distance of the center of mass from the support limb in both the medial and posterior

~48~

directions (Table 4-1). In a previous study of video footage of ACL injuries, Teitz

reported that during the injury event, the position of the COM of the subject was posterior

and farther from the location of support limb (Teitz 2001), suggesting that an increase in

the position of the COM in the medial and posterior directions might also increase the

risk for ACL injury. The results from this study suggest that the risk for ACL injury is

increased on the high friction, high speed condition because of the increase in the internal

rotation moment combined with a higher flexion moment and a large adduction moment

while the knee is flexed less than 30°, and an change in the position of the COM in the

medial and posterior directions.

Altogether, the adaptations observed in all the subjects on the high friction surface

at low and high speeds suggest that the biomechanical causes for the higher incidence of

ACL injury on high friction surfaces changes based on the speed of the maneuver.

Cochrane et al. studied injuries in Australian football and determined that running speed

was not correlated to an increase in ACL injury rates as injuries occurred at speeds from

slow jogging to fast running/sprinting (Cochrane 2007). However, other studies have

suggested that ACL injuries occur more frequently during high speed run to cut

maneuvers (Myklebust 1998), especially on a high friction rubber surface (Pope 2002),

suggesting that increased speed does increase the risk for ACL injury. The current study

suggests that the biomechanical factors that could cause the increased incidence of ACL

injuries on a high friction surface at high speeds are an increased internal rotation

moment at the knee combined with a high flexion moment and a large adduction moment

with the knee under 30° of knee flexion, along with the increase in the medial and

posterior distance of the COM from the support limb; the previous study (Dowling 2010)

suggests that at low speeds, a lower knee flexion angle, a higher external knee flexion

and knee abduction moment, and greater medial distance of the center of mass from the

support limb could cause the increased incidence of ACL injury. Therefore, the

discrepancies noted in the speed of maneuver during an injury could be related to the

different biomechanical variables that influence the risk for injury at different speeds.

The differences in the movement strategies between females and males suggest

that women are more at risk for ACL injury during all three trial conditions. These results

support the large body of previous literature suggesting that women are more at risk for

~49~

ACL injury than men (Renstrom 2008). This study showed that females had less knee

flexion at foot contact than men for all three conditions, and decreased knee flexion

during foot contact has been previous suggested as a major risk factor for ACL injury

among women (Boden 2000; McNair 1990; Olsen 2004; Renstrom 2008). Additionally,

the trend towards an increased knee abduction angle during foot contact displayed by the

subjects in this study supports previous work suggesting that an increased abduction

angle is a risk factor for ACL injury and that women display a greater abduction angle

than men for the same movement tasks (Ford 2005; Hewett 2005a; Hewett 2006; McLean

2004; McLean 2005; Olsen 2004; Renstrom 2008; Silvers 2007). Altogether, females are

at a greater risk for ACL injury than their male counterparts on low friction surfaces at

low speeds as well as on high friction surfaces at both low and high speeds because their

movement adaptations (decreased knee flexion angle, increased knee abduction angle)

are known to increase the risk of injury.

One consideration in evaluating the results of this study is that the increased speed

of the run to cut maneuver was not as fast as standard game running, and athletes in

playing situations can experience much faster running conditions. The maximum speed of

the subjects was constrained due to the smaller size of the testing facility, and to reduce

the risk of injury for the subjects. The results measured in this study can be extrapolated

to higher speeds, but clearly indicate that speed does change the movement strategies of

the subject. Additionally, when the subject cohort was split by gender the statistical

power of the data was decreased because there were significantly fewer subjects in each

category. However, the results seen for the knee flexion angle were statistically

significant and support previous research on gender and ACL injury, as does the trend

towards a change in the knee abduction angle. Even with the gender limitations, the

results observed in this study should be considered as potential contributing factors to the

increased incidence of ACL injury in high friction conditions and among females.

4.6. Conclusion This study supports the hypotheses that increasing running speed prior to a single

limb landing combined with increased floor friction alters a subject’s movement in

biomechanical measures associated with risk for ACL injury, and these changes are

~50~

altered more in females than males. This study found that the high speed, high friction

condition resulted in an increased knee flexion angle, increased knee flexion, adduction,

and internal rotation moments, and a greater medial and posterior distance of the center

of mass from the support limb. Furthermore, the differing adaptations to the high friction

surface observed at different speeds suggest that the biomechanical causes for the higher

incidence of ACL injury on high friction surfaces change based on the speed of the

maneuver. In terms of gender, for every condition females exhibited significantly lower

knee flexion angles than their male counterparts and showed a trend towards an increased

knee abduction angle, suggesting that they are more at risk for ACL injury during all the

conditions. This study provided additional insight into the influence of speed, gender, and

shoe-surface friction on the risk for ACL injury.

4.7. Acknowledgments The authors thank the volunteer subjects for their participation. Special thanks to

students Erica Holland, Nathan Fenner, Katerina Blazek, and Jenssy Rojina for their

assistance in collecting and processing the data. This research was supported by the

National Science Foundation grant #03225715.

~51~

55 A Wearable System to Assess Risk for ACL Injury During Jump Landing: Measurements of Temporal Events, Jump Height, and Sagittal Plane Kinematics

5.1. Overview The incidence of anterior cruciate ligament injury (ACL) remains high, and

there is a need for simple, cost effective methods to identify athletes at a higher risk

for ACL injury. Wearable measurement systems offer potential methods to assess the

risk of ACL injury during jumping tasks. The objective of this study was to assess the

capacity of a wearable inertial-based system to evaluate ACL injury risk during

jumping tasks. The system accuracy for measuring temporal events (initial contact,

toe-off), jump height, and sagittal plane angles (knee, trunk) was assessed by

comparing results obtained with the wearable system to simultaneous measurements

obtained with a marker-based optoelectronic reference system. Thirty-eight healthy

participants (20 male and 18 female) performed drop jumps with bilateral and

unilateral support landing. The mean differences between the temporal events obtained

with both systems were below 5 ms and the precisions were below 24 ms. The mean

jump heights measured with both systems differed by less than 1 mm, and the

associations (Pearson correlation coefficients) were above 0.9. For the discrete angle

parameters, there was an average association of 0.91 and precision of 3.5° for the knee

~52~

flexion angle and an association of 0.77 and precision of 5.5° for the trunk lean. The

results based on receiver-operating characteristic (ROC) also demonstrated that the

proposed wearable system could identify movements at higher risk for ACL injury.

The area under the ROC plots was between 0.89 and 0.99 for the knee flexion angle

and between 0.83 and 0.95 for the trunk lean. The wearable system demonstrated good

concurrent validity with marker-based measurements and good discriminative

performance in terms of the known risk factors for ACL injury. This study suggests

that a wearable system could be a simple cost-effective tool for conducting risk

screening or for providing focused feedback.

Portions of this chapter have been accepted for publication in the Journal of

Biomechanical Engineering by ASME: “A wearable system to assess risk for ACL

injury during jump landing: measurements of temporal events, jump height, and

saggital plane kinematics.” Dowling AV, Favre J, Andriacchi TP. ©2011 (in press).

The author contributed to this paper by collecting all of the data from the subjects,

processing the data, analyzing the data, and writing the manuscript of the paper.

5.1.1. List of Definitions

ACL anterior cruciate ligament

Xw (subscript w) wearable system

Xr (subscript r) reference system

LP landing preparation prior to initial contact

IC initial contact

MAX peak value

DIF difference between peak and initial contact (DIF = MAX - IC)

ED end of the deceleration phase

TO toe-off

A1w first peak of the maximum inferior shank acceleration

A2w maximum value of the shank acceleration norm after initial contact

ΔIC systematic delay at initial contact (ΔIC = ICr - ICw)

ΔTO systematic delay at toe-off (ΔTO = TOr - TOw)

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Δh vertical displacement (h) between the normal posture and toe-off

posture

KFE knee flexion/extension angle

TL trunk lean

ROC receiver-operating characteristic

5.2. Introduction

As described in Chapter 1, the ACL is frequently injured and can lead to

premature knee osteoarthritis with or without reconstruction. Qualitative analyses of

ACL injuries during sporting events suggest that one of the major mechanisms for

non-contact ACL injuries is a landing from a jump with either one or two legs. In

order to study this injury mechanism, researchers have designed a physical task,

known as a drop jump, which can replicate the injury mechanism in a safe controlled

laboratory setting (Ford 2003; Noyes 2005). This drop jump task has been employed

in numerous studies focused on understanding ACL injuries (Hewett 2005a; Hewett

2006), and the International Olympic Committee Medical Commission recommends

using this task to identify athletes at high risk for ACL injury (Renstrom 2008).

Quantitative analyses of ACL injuries indicate that specific kinematic measures during

jumping tasks can be used to identify a higher risk for ACL injury; specifically, a

small flexion angle and a small trunk lean angle (Chapter 2).

In terms of novel motion analysis systems, numerous wearable measurement

systems have been proposed in order to simplify the measurement of human

movements and to allow monitoring of subjects in their natural environments

(Aminian 2006). Although these systems rely on both the initial contact and the toe-

off from the jump events, no temporal error was reported regarding the detection of

these events. However, these two time points, as well as the end of the deceleration

phase, are critical for reducing the continuous knee joint angle measurements into the

discrete kinematic parameters that are related to the risk of ACL injury.

The objective for this study was to assess the capacity of a wearable inertial-

based system to measure jumping tasks as well as to assess the measurement error and

~54~

the capacity of the system to evaluate ACL injury risk. The measurement errors for

temporal event detection, jump height and sagittal plane knee and trunk kinematics

were evaluated by comparing simultaneous measurements from the wearable system

with a marker-based optoelectronic system for two jumping tasks, bilateral and

unilateral drop jumps. Then, the discriminative performance of the wearable system to

identify the movements at higher risk for ACL injury was analyzed.

5.3. Methods 5.3.1. Subjects

Thirty-eight participants volunteered for this investigation. There were 20 male

and 18 female subjects with an average age of 26.9 ± 4.3 years and BMI of 23.0 ± 2.1.

The subjects were regular participants in sports involving jumping maneuvers at the

recreational/intramural level in order to ensure that they would be familiar with

jumping tasks. Subjects with previous lower limb musculoskeletal injuries requiring

surgery or any current symptoms of pain or injury were excluded. This study was

approved by the Institutional Review Board and informed written consent was

obtained from all subjects prior to data collection.

5.3.2. Experimental Design

The subjects performed two different drop jump maneuvers in a gait

laboratory: a drop jump with a bilateral support landing and a drop jump with a

unilateral support landing. For both drop jump tasks, the subject started the maneuver

by standing on a box (36 cm high for bilateral, 20.5 cm for unilateral) in their normal

standing posture. At a signal from the investigator, they dropped directly off the box

and then immediately performed a maximum height vertical jump, raising both arms

to touch a target placed above their heads (Noyes 2005, Ford 2003). For the unilateral

support landings, subjects shifted their weight to their right leg, dropped off the box,

landed, and then performed the vertical jump with their right leg. The left leg never

impacted the ground during this jump task. The landing directly after the drop from

the box wa

would impa

subjects lan

landing, onl

each jump t

trial to prev

these jumps

simultaneou

of a camera

s used for t

act the force

nded on two

ly the right

task prior to

vent fatigue

s, measurem

usly by the p

-based motio

F

the analysis,

plates in the

adjoining fo

force plate

o data collec

. Three tria

ments of low

proposed we

on capture sy

Figure 5-1: P

~55~

, and the bo

e floor during

orce plates, o

was used. T

ction and w

als were rec

wer body ki

arable system

ystem comb

Proposed w

~

ox was posi

g this landin

one foot on e

The subject

were given a

orded for b

inematics an

m and by a r

bined with tw

wearable sys

itioned so th

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each plate; fo

ts were allow

rest interva

both jumping

nd kinetics

reference sy

wo force plat

stem.

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for the unilat

wed to prac

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g tasks. Dur

were collec

ystem consis

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ects

ding,

teral

ctice

each

ring

cted

ting

5-1).

~56~

5.3.3. Wearable System

5.3.3.1. Hardware

The wearable system (hereafter referred as subscript w) used for this study

consisted of a light portable data logger and three miniature IMUs (Physilog®,

BioAGM, CH). Two IMUs, containing a three axis gyroscope (±900°/s) and a three

axis accelerometer (±7g), were fastened to rigid lightweight plastic plates, and then

each plate was affixed onto the right thigh and shank of the subjects with elastic straps

(Favre 2010, Favre 2008). The last IMU was affixed to the chest of the subject on the

manubrium with tape. This IMU contained one gyroscope (±600°/s) that measured the

angular velocity in the sagittal plane, and two accelerometers (±7g) that measured

vertical and frontal trunk accelerations. To avoid marker occlusion with the reference

system, the data logger was fixed on the upper back using a custom holder. The

inertial sensors were recorded at 240 Hz and downloaded to a host computer at the end

of the testing session. To synchronize the data, the wearable system was connected to

the reference system by a thin cable.

5.3.3.2. Angle measurements

Multiple calibrations were performed to align the IMUs with the bone

anatomical frames and to remove error related to the positioning of the IMUs on the

subjects. For the two leg-mounted sensors, a functional calibration procedure (Favre

2009), consisting of seated passive knee movements, was performed. In order to have

a common reference frame for the thigh and shank segments, an alignment procedure

consisting of a standing hip abduction movement was completed before the jumping

tasks (Favre 2008). For the trunk, the bone anatomical frame was defined by the

vertical axis (gravity) as measured by the chest accelerometers during a neutral

standing posture and by the medial/lateral axis of the chest IMU. During the

movements, the orientation of the three segments relative to their own reference

frames were calculated using a fusion algorithm based on the acceleration and angular

velocity signals (Favre 2006). The knee flexion/extension angle (KFEw) was

calculated based on the thigh and shank orientations using Cardan angles (Grood

~57~

1983), and the trunk lean (TLw) was defined as the angle between the vertical and the

inferior/superior axis of the trunk frame.

5.3.3.3. Temporal events detection

The first peak (A1w) of the maximum inferior shank acceleration was used to

identify the initial contact event (ICw). Similarly, the maximum value (A2w) of the

shank acceleration norm after ICw was selected for the toe-off event (TOw). Since

these acceleration peaks were measured on the shank, systematic delays (ΔIC and ΔTO)

were expected with the actual events (ICw = A1w + ΔIC; TOw = A2w + ΔTO). In order to

objectively estimate the ΔIC and ΔTO delays, the jumps were randomly divided into

two groups. The first group was used to determine the delays, and the second group

was used to evaluate the method. The delays were defined as the mean value of the

difference between the occurrence of the acceleration peaks and the occurrence of the

events measured with the reference system (hereafter referred as subscript r) for all

jumps in the first group (ΔIC = ICr - ICw; ΔTO = TOr - TOw). For the rest of the study,

all ICw and TOw events were identified based on the acceleration peaks combined with

the delays (ΔIC = 41.7 ms; ΔTO = 41.7 ms). Finally, the end of the deceleration phase

(EDw) was defined as the time point when the maximum knee flexion angle occurred

between ICw and TOw (Ford 2010, Yu 2006). Similarly to ICw and TOw, the systematic

delay for EDw as compared to the reference event was determined by dividing the total

number of jumps into two groups (ΔED = -16.7 ms).

5.3.3.4. Vertical jump height

For this study, the height of the vertical jump after the landing from the box

was measured. In the literature, two approaches are used to estimate the height of a

jump. The first approach consists of measuring the flight time and then using a

ballistic formula (height = 1/8 * flight time * g; g= 9.81m/s2) to calculate what is

known as the vertical jump flight height (Bosco 1983). The second approach,

considered the gold standard (Aragon 2000), consists of performing a direct

measurement of the movement of the center of mass using a motion capture system. A

~58~

fundamental difference between the two approaches is whether or not the vertical

displacement (Δh) from the heel-off during the propulsion phase is included in the

measurement. At toe-off, the body is already in an extended position compared to the

normal standing posture, mostly as a result of ankle plantar flexion. In this study, an

intermediate approach was selected to provide the total jump height. First, the vertical

jump flight height was calculated using the ballistic formula; flight time was defined

as the duration between the take-off (TOw) and the final landing. The final landing was

determined by the same method as ICw, using the inferior shank acceleration and ΔIC.

Then, using the reference system, Δh was determined for each jump by subtracting the

sacrum height during the neutral posture from the sacrum height at take-off (TOr). No

correlation was found between Δh and the morphology of the subjects, and so all the

values for Δh were averaged for each jump type. These averages (10.6 cm for bilateral

and 12.4 cm for unilateral) were added to the vertical jump flight height to obtain the

total vertical jump height.

5.3.4. Reference System

An optoelectronic motion capture system (Qualisys Medical, Gothenburg, SE)

with ten infrared cameras collecting at 120 Hz was used to measure the motion of 28

primary reflective markers (trunk: left and right medial clavicle; pelvis: left and right

anterior superior iliac spine, left and right iliac crest, and left and right posterior

superior iliac spine; right thigh: great trochanter plus nine markers distributed on the

front and lateral sides; right shank: lateral tibial plateau plus six markers distributed on

the front and lateral sides; right foot: lateral malleolus, lateral heel, and fifth

metatarsal) and 4 auxiliary markers (medial and lateral femoral condyles, medial tibial

plateau and medial malleolus). Two multi-component force plates (Bertec, Columbus,

OH) measured the subjects’ interaction with the ground at 1200 Hz. Prior to

completing the jumping tasks, the subjects were measured during a neutral standing

reference posture and a thigh circumduction movement with both primary and

auxiliary markers. The auxiliary markers were then removed to allow unencumbered

execution of the jumping movements. The point cluster technique was used to track

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the orientation of the pelvis, thigh, shank, and foot technical frames using the

corresponding primary markers (Andriacchi 1998). The thigh bone anatomical frame

was defined according to Cappozzo et al. (Cappozzo 1995), and the shank bone

anatomical frame was based on Dyrby and Andriacchi (Dyrby 2004). The hip joint

center was estimated from the circumduction movement using an optimization method

(Halvorsen 2003). Similarly to the wearable system, Cardan angles based on the thigh

and shank bone anatomical frame (Grood 1983) were used to describe the knee

flexion/extension angle (KFEr) during the entire jumping trial. The trunk lean (TLr)

was defined as the angle between the vertical axis and the axis joining the middle

point of the clavicle markers and the middle point of the anterior superior iliac spine

markers (Foti 2000). In order to compare the wearable and the reference angles, the

reference angles were reset during the neutral posture. The ICr and TOr events were

defined as the time points when the vertical ground reaction force exceeded and fell

below 10N respectively (Ford 2010). According to Ford et al. (Ford 2010), the EDr

event was defined as the time point corresponding to the minimum vertical height of

the pelvis between ICr and TOr. To measure the height of the vertical jump, the

position of a virtual sacrum marker was calculated by averaging the left and right

posterior superior iliac spine markers. The jump height was defined as the difference

between the height of the sacrum during the neutral posture and the maximum sacrum

height achieved during the vertical jump.

5.3.5. Data Analysis

To assess the event detection method, the difference between the time points

obtained with the wearable and the reference systems (e.g., ICw and ICr) was

calculated for all the jumps in the evaluation group. Then, for the bilateral and

unilateral jumping tasks the mean (accuracy) and standard deviation (precision) of

these differences were calculated for the three events.

The total vertical jump height measured with the wearable system was

compared to the reference jump height in terms of association and level of agreement

for both bilateral and unilateral jumping tasks. The Pearson correlation coefficient (R)

~60~

was used to characterize the association, whereas a Bland and Altman plot was used to

characterize the agreement (Bland 1999). In the presence of heteroscedasticity (p<

0.05), the accuracy and precision were calculated as the summation of a constant term

and a variable term (calculated as a percentage of the measurement). To evaluate the

concurrent validity of the measurement methods (i.e., the ability to distinguish

between groups), a paired two-tailed t-test with landing task as the inter-test factor was

used to identify significant jump height differences between the two jumping tasks.

This test was performed independently for the wearable and the reference systems.

The knee flexion angle and the trunk lean were analyzed in terms of both

pattern and amplitude. For the pattern, the similarity between the curves obtained with

both systems (e.g., KFEw and KFEr) was evaluated with the Pearson coefficient of

correlation (R). This coefficient was calculated for both angles (KFE and TL) during

each jump for the entire movement, from the drop off the box until the final landing.

The median, 25th percentile, and 75th percentile of these coefficients were then

calculated for both jumping tasks. To assess the amplitude, discrete values related to

ACL injury risk were extracted from the continuous angles. Similar to the jump

height, the values obtained with the wearable and the reference systems were

compared in term of association, agreement and concurrent validity. For the knee

flexion angle, four discrete values were considered: the angle at initial contact

(KFE(IC)), the maximum flexion angle achieved during the deceleration phase of the

landing (KFE(MAX)), the difference between the maximum flexion angle and the

flexion angle at initial contact (KFE(DIF) = KFE(MAX) - KFE(IC)), and the

minimum flexion angle attained prior to initial contact, or the landing preparation

angle (KFE(LP)). Three values were extracted for the trunk lean (TL(IC), TL(MAX)

and TL(DIF)).

Since it has previously been shown that kinematics measured with different

systems are generally not interchangeable (Ferrari 2008), differences in the knee

flexion angle and trunk lean measurements between the wearable and the reference

systems were expected. Thus, the receiver-operating characteristic (ROC) was

calculated to assess the performance of the proposed wearable system in identifying

~61~

movements at higher risk for ACL injury (Zweig 1993). Again, because the angles can

be dependent on the measurement system, it was impractical to use previous

publications to set thresholds that would differentiate between “at risk” or “not at risk”

jumps for the reference system. Therefore, based on the literature described in the

introduction, a direction of rotation associated with a higher risk for injury was

identified for the two angles (i.e., knee extension and backward trunk lean). Then, for

each discrete angular parameter for both jump tasks, the median value was calculated

for the reference system in order to divide the movements into two groups, a higher

risk and lower risk cohort. These cohorts were used to plot the ROC of the wearable

system for each discrete angular parameter for both jump tasks. Finally, the areas

under the ROC plots were calculated to characterize the discriminative performance of

the wearable system (Zweig 1993). The values for the area under a ROC plot range

from 0.5 to 1, with 1 indicating perfect separation of the two groups with the wearable

system. All statistical tests were performed in MATLAB version R2009b (The

Mathworks, Natick, MA) and the significance level was set a priori to α = 0.05.

5.4. Results The temporal differences between the events obtained with the wearable

system and the events obtained with the reference system differed by less than 1% of

stance, and the precisions varied between 2% and 4.5% of stance. Specifically, the

mean (standard deviation) differences were -3.0 (9.1) ms for IC, -4.9 (23.0) ms for

ED, and -0.1 (13.8) ms for TO, and the duration of stance averaged 485 ms.

The bilateral and unilateral jump heights measured with both systems differed

an average of less than 1 mm, and the associations (R) between the measurement

systems were above 0.9 (Table 5-1). Heteroscedasticity was present for the unilateral

jump height, as shown in Figure 5-2. The accuracies for the averaged bilateral and

unilateral jump heights were -0.6 cm and -5.9 cm, whereas the precisions were 1.9 cm

and 1.4 cm (Table 5-1). Regarding the concurrent validity, both systems reported that

the bilateral jump heights were significantly larger than the unilateral jump heights (p

< 0.001).

Jump

Bilateral Unilateral

Table ^p < 0.001**p < 0.00

Figure 5-2b

The

are presente

(Ferrari 201

high for th

Regarding t

average asso

0.77 wherea

performed

ReferencMean (cm)

38.2** 27.0**

5-1: Jump h: significant

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10a), the sim

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ant differenc

d Altman anshed lines co

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cal bilateral

milarity of t

xion (KFE)

de (Table 5-3

0.91 and pre

sion was 5.5

nd indicated

~62~

arable Can

m) SD

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sured with wn between mce between j

nalysis of juorrespond t

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ump height. to 66% limit

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001)

~63~

differences between bilateral and unilateral jumps except for TL(MAX), where a

significant difference was measured with the wearable system but not the reference

system, and TL(DIF), where the reference system indicated a significant different but

not the wearable system. Indeed, for the unilateral jump compared to the bilateral

jump, both systems reported that subjects had less knee flexion for all discrete values.

The area under the ROC plots for the wearable system were between 0.89 and 0.99 for

KFE and between 0.83 and 0.95 for TL at identifying the jumps that were considered

as being at risk for ACL injury (Table 5-3).

Figure 5subje

T

-3: Examplect during on

Knee Flexion

Trunk Lean

Table 5-2: Si

e of continune bilateral

para

Bilatera

Unilatera

Bilatera

Unilatera

imilarities ofor the con

~64~

uous knee fll jumping taameters ide

Median

al 0.999

al 0.997

al 0.656

al 0.786

of the patterntinuous kne

~

lexion angleask, with theentified.

n 25%

0.998

0.996

0.511

0.404

rns (R) betwee joint ang

e and trunk e discrete ti

75%

0.999

0.998

0.813

0.885

ween the sysgles.

lean for onime point

stems

e

~65~

Measured Angle Values System Evaluation

Reference Wearable Association Error

Risk Determination

Parameter Jump Mean (°) SD Mean (°) SD R Accuracy (°) Precision (°) ROC

Kne

e F

lexi

on (

KF

E) Land Prep

Bilateral 12.2* 10.2 15.9* 10.1 0.97^ -3.7 2.4 0.99 Unilateral 9.2* 6.7 12.8* 6.9 0.93^ -3.6 2.6 0.96

Contact Bilateral 21.3** 8.5 26.0** 8.0 0.92^ -4.7 3.4 0.96

Unilateral 12.0** 6.2 18.1** 6.6 0.84^ -6.0 3.6 0.90

Max Stance

Bilateral 85.3** 10.5 87.7** 10.7 0.93^ -2.4 3.9 0.96 Unilateral 62.4** 7.5 66.6** 8.1 0.85^ -4.1 4.3 0.89

Difference Bilateral 64.0** 10.9 61.8** 11.4 0.94^ 2.3 3.9 0.96

Unilateral 50.4** 7.1 48.5** 7.8 0.89^ 6.6 -9.5% 3.4 0.92

Tru

nk L

ean

(TL

)

Contact Bilateral 9.3* 9.2 13.8** 10.2 0.75^ -4.6 6.8 0.83

Unilateral 11.6* 7.0 11.3** 8.9 0.65^ 3.6 -28.6% 6.3 0.83 Max

Stance Bilateral 22.3 12.6 26.9* 11.1 0.88^ -7.8 13.1% 5.8 0.93

Unilateral 22.1 12.0 24.9* 10.7 0.88^ -5.5 11.7% 5.6 0.95

Difference Bilateral 13.1** 7.6 13.1 7.1 0.72^ 0.0 5.4 0.87

Unilateral 10.3** 7.3 13.5 7.0 0.76^ -3.1 3.0 13.8% 0.89

Table 5-3: Knee kinematic parameters measured at specific time points with both measurement systems. ^p < 0.001: significant correlation between measurement systems for same jump task

*p < 0.01, **p < 0.001: significant difference between jump task for same measurement system

~66~

5.5. Discussion Specific knee kinematic parameters during drop jumps can potentially identify

subjects at higher risk for ACL injury. However, these parameters are difficult to

measure, which limits their usefulness as a tool for risk screening or to provide focused

feedback. The purpose of this study was to propose a simple, wearable IMU-based

measurement system for analyzing drop jumps and to evaluate the efficacy of this

wearable system at identifying movements associated with a higher risk for ACL injury.

To this end, approximately two hundred jumps were simultaneously recorded with both

the wearable system and a reference system. The temporal events, vertical jump height,

and sagittal plane angles were evaluated in order to characterize this new system.

The method proposed to detect the ground contact events (IC and TO) appears to

have the capacity to detect parameters related to the risk of ACL injury. The high

accuracy (below 5 ms) and precision (below 24 ms) confirmed that the selected shank

acceleration features could be reliably identified and that there was a systematic delay

between the temporal events based on shank acceleration relative to the foot-ground

event. The precision error was about twice as high for the end of the deceleration event

(ED). This was expected because this event is not associated with a shock, but is based on

the displacement of the sacrum or on the knee flexion angle, which are both smooth

curves. Other authors have previously proposed the use of accelerometers to detect the

ground contact events during jumping tasks (Quagliarella 2010, Casartelli 2010, Elvin

2007). However, these studies did not report the errors related to the detection of the

events. Correctly identifying IC is important because the angles at this particular time

point are necessary to assess the risk for ACL injury, while a larger error for ED is

acceptable because this event is only used to define the temporal window during which

the maximum rotations are determined. Therefore, this study confirmed that the proposed

wearable system can detect the temporal events that are required to evaluate the risk of

ACL injury.

The total vertical jump heights measured with both systems were strongly

associated and had a high level of agreement (Figure 5-2 and Table 5-1). These results

confirmed the efficacy of the method in detecting the temporal events, as well as the use

of constant offsets to account for the vertical displacement during heel-off (Δh). The two

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Δh used in this study were of comparable amplitude with the systematic error previously

reported between devices that measured the flight jump height and the total jump height

(Aragon-Vargas 2000). The offsets were independent of the subjects, indicating that

either the total jump height or the flight jump height could be used for evaluation. The

heteroscedasticity observed for the unilateral jump task might be a result of the

asymmetric nature of this task or because there is a difference in pelvis posture between

take-off and landing. In comparison to Castrelli et al. (Castrelli 2010), who assessed a

system consisting of one accelerometer worn at the hip, the system proposed in this study

appeared more accurate and slightly more precise. In addition, the proposed wearable

system reported comparable precision with optical devices specifically designed to

measure flight jump height (Bosquet 2009, Glattorn 2010). The 11cm difference in jump

height between the two jumping tasks was expected since the athletes generate much less

vertical force jumping from one leg as opposed to two legs. While jump height is not

specifically a risk factor for ACL injury, it is important as a performance measurement.

The athletes who would potentially be using a wearable system for risk screening or

feedback during a training program would also certainly be interested in monitoring their

jump height. In conclusion, this study demonstrated that an IMU affixed to the shank is

an appropriate alternative for the measurement of vertical jump height and has an

advantage over standard systems because this method does not rely on a specific testing

environment and so can be used anywhere.

The associations in angle patterns between the two systems suggested that the

wearable system can measure differences in angles between subjects (Table 5-2).

However, there were small differences in the amplitude of the measurements. These

differences were expected since both systems performed completely independent

measurements; each system used a different method to track the orientation of the

segments and a different definition for the bone anatomical frames. It has been previously

shown that the choice of bone anatomical frame can strongly influence the knee angles

(Favre 2010, Ferrari 2008, della Croce 1999). In addition, to measure the orientation of

the leg segments, the wearable system used a rigid plate strapped to the subject while the

reference system used two clusters of markers as well as an algorithm to reduce the soft

tissue artifacts (Andriacchi 1998). The trunk was assumed to be a rigid segment and did

~68~

not account for concavity of the body. However, the pattern similarities reported in this

study for jumping tasks were comparable to the similarities observed with other inertial-

based systems during walking tasks (Ferrari 2010b, Favre 2009, Favre 2008). The small

differences could be explained by the higher intensity of the jump tasks as well as

differences in the study design.

For both the knee flexion and the trunk lean, the angles were similar between the

wearable and the reference systems and consistent with previous studies that measured

knee and trunk sagittal angles during bilateral drop jumps (Blackburn 2008, Blackburn

2009, Hewett 2005a, McLean 2007, Ford 2010). Like the angular patterns, the precisions

reported in this study for jumping tasks were similar to the precisions observed with other

IMU-based systems during walking tasks (Favre 2009, Favre 2008, Picerno 2008). These

results agree with previous work showing that rotations measured with different methods

are not automatically interchangeable (Ferrari 2008). But in terms of risk for ACL injury,

the concurrent validity and the ROC are more critical than the actual amplitudes of

rotation.

The wearable system had a similar capacity to detect differences between the two

jump types when compared to the reference system, providing important evidence for the

application of the wearable system. Specifically, both systems reported the same

direction of change for all of the significantly different measurements except for one.

The ability of the wearable system to distinguish between two different jumping tasks

suggests that the wearable system should also be able to identify kinematic differences

between low risk and high risk jumping movements. The area under the ROC plots

confirmed this assumption and suggest that the wearable system can identify movements

classified as higher risk by the reference system. It is worth mentioning that the area

under the ROC plots is only an evaluation, since the median thresholds of the reference

system used to separate the movements into the high and low risk cohorts were not based

on data collected from actual injuries. Finally, as shown by Hewett et al. (Hewett 2005a),

combining individual risk factors together can increase the efficacy of the prediction for

ACL injury.

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5.6. Conclusions The wearable system proposed in this study extended the functionality of inertial-

based systems to analyze jumps. It accurately detected crucial temporal events and

measured total jump height with a precision comparable to dedicated optical devices.

Additionally, the proposed system measured the knee flexion and the trunk lean, and

demonstrated good concurrent validity and discriminative performance in terms of the

known risk factors for ACL injury. Wearable systems offer many advantages over

traditional motion capture systems: they are simpler to use, do not require complex post-

processing, and make it feasible to test subjects in a natural environment. These

advantages, combined with the results reported in this study, suggest that a wearable

system could be a promising tool for conducting risk screening or for providing focused

feedback.

5.7. Acknowledgments This work was supported by an NSF graduate fellowship, the Palo Alto VA, and

the Stanford Center on Longevity. Thanks to Dr. Kamiar Aminian from EPFL for his

assistance.

~70~

66 Characterization of Jump Landing Mechanics Based on Thigh and Shank Segment Angular Velocity: Implications for ACL Injury Risk

6.1. Overview The dynamic movements associated with anterior cruciate ligament (ACL)

injury suggest that limb segment angular velocity can provide important information

for understanding the conditions that lead to an injury. Angular velocity measures

could provide a quick and simple method of assessing injury risk without the

constraints of a laboratory. The objective of this study was to test if there is an

association between the thigh and shank angular velocities and the knee abduction

moment (a measure relevant to ACL injury). Thirty-six healthy participants (18 male)

performed drop jumps with bilateral and unilateral support landing. Thigh and shank

angular velocities were measured by a wearable inertial-based system, and external

knee moments were measured using a marker-based system. The angular velocity

parameters were able to distinguish between the two types of jumping tasks.

Furthermore, the coronal angular velocities were significantly correlated with the knee

abduction moment. The receiver-operating characteristic, used to determine the ability

of the segment angular velocity to identify movements at higher risk for ACL injury,

ranged from 0.57 to 0.78. This study showed that angular velocity could be a useful

~71~

parameter to analyze ACL injuries and that the coronal angular velocity is associated

with the knee abduction moment.

Portions of this chapter were submitted for publication to the Journal of

Orthopaedic Research by Wiley Periodicals: “Characterization of jump landing

mechanisms based on thigh and shank segment angular velocity: implications for ACL

injury.” Dowling AV, Favre J, Andriacchi TP. ©2011 (in review). The author

contributed to this paper by collecting all of the data from the subjects, processing the

data, analyzing the data, and writing the manuscript of the paper.

6.2. Introduction As described in Chapter 1, the ACL is frequently injured and can lead to

premature knee osteoarthritis. Qualitative analyses of ACL injuries during sporting

events suggest that one of the major mechanisms for non-contact ACL injuries is a

landing from a jump with either one or two legs. In order to study this injury

mechanism, researchers have designed a physical task, known as a drop jump, which

can replicate the injury mechanism in a safe controlled laboratory setting (Ford 2003;

Noyes 2005). Quantitative analyses of ACL injuries indicate the external knee

abduction moment has been reported to be a strong indicator of injury risk (Chapter 2).

Like joint moments, angular velocity could also be an important metric in

understanding the neuromuscular control and the mechanism of ACL injury. For

example, Yu et al. (Yu 2006) found that sagittal hip and knee angular velocity were

correlated to the knee joint resultant anterior-posterior force during landing from a

jump, suggesting that angular velocity could be a critical factor that affects ACL

loading. Segment angular velocity (SAV), which is complementary to joint angular

velocity, might be a more important metric in understanding the mechanism of jump

landing because it describes the movement of each segment independently (Favre

2010). Lower limb SAV has been shown to be a valuable outcome parameter for

various applications of gait analysis (Mills 2001, Cham 2002, Salarian 2004, Damiano

2006, Favre 2010). Moreover, during the landing from a drop jump, the coronal thigh

and shank SAVs might be associated with knee abduction moment since they are

~72~

closely related to the medial-lateral movement of the knee. Therefore, an assessment

of the ability of the SAVs to predict the knee abduction moment could establish SAV

as a surrogate marker for the knee abduction moment, thus enabling simple and

efficient testing for ACL injury risk. Although it might provide insight into the

understanding of the ACL injury mechanism, SAV has not been previously reported

for the lower limb segments during drop jump landing tasks.

As discussed in Chapter 2, properly measuring ACL injury risk factors requires

complex instrumentation. Some protocols have been proposed to analyze drop jumps

based on manual inspection of standard video recordings (Padua 2009b; Myer 2010b;

Myer 2010c). While these methods are simple, they still require time to analyze the

videos and might not be able to quantify subtle movements. Easy-to-use wearable

systems composed of inertial measurement units (IMUs) attached on the thigh and

shank segments have been proposed to measure knee rotation (Favre 2009). Since

these IMUs contain a three axis angular rate sensor (gyroscope), they can also be used

to measure SAV with a high degree of reliability. Using an IMU-based system, Favre

et al. (Favre 2010) recently showed that three-dimensional lower limbs SAVs are

consistent among subjects during gait and that SAV could be used to compare ankle

treatments.

The primary objective of this study was to test the hypothesis that there is an

association between coronal SAV and the knee abduction moment, and if so, to

evaluate the potential of SAV to identify the movements at higher risk for ACL injury.

In addition, this study characterized the inter-subject variations of the thigh and shank

SAVs during a jump landing and evaluated the sensibility of SAV at distinguishing

different landing mechanisms.

6.3. Methods 6.3.1. Subjects

Eighteen males and eighteen females, with an average age of 26.7 ± 4.1 years

and BMI of 23.0 ± 2.2, volunteered for this study. All were regular participants in

sports and did not have any history of previous lower limb musculoskeletal injuries

~73~

requiring surgery or any current injuries. This study was approved by the Institutional

Review Board and informed written consent was obtained from all subjects prior to

data collection.

6.3.2. Experimental Design

The subjects performed bilateral and unilateral support drop jump maneuvers

in a gait laboratory (Ford 2003, Noyes 2005). The subjects dropped directly off a box

(36cm for the bilateral, 20.5cm for the unilateral) and then immediately performed a

maximum height vertical jump. For the unilateral jumps the subjects landed on their

right leg. The landing directly after the drop from the box was used for the analysis.

Two force plates were used to record the subject’s landing, one for each foot. The

subjects practiced the jump tasks prior to data collections. Three trials were recorded

for both jumping tasks. During these jumps, the movement of the lower limbs was

measured simultaneously by a wearable system and by a camera-based motion capture

system combined with the two force plates (Figure 6-1).

Figure 6-system maconvention

6.3.3. S

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~74~

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from

~75~

the fixation of the IMUs on the body segments (Favre 2009, Favre 2008). As a result,

the SAVs were expressed relative to a right handed coordinate system embedded in

each body segment. The sagittal, coronal, and transverse SAVs corresponded to the

angular velocity around the medial/lateral, posterior/anterior, and inferior/superior

axes respectively of the embedded coordinate systems (Figure 6-1).

For the analysis, the continuous thigh and shank SAVs were restricted to the

stance phase following the drop off the box. The initial contact (IC) and toe-off (TO)

events were detected using characteristic peaks of the shank acceleration (Elvin 2007),

and the end of the deceleration phase was defined as 50% of stance. Based on previous

studies (Myer 2009, Hewett 2005), three critical points on the curves were selected to

reduce the continuous SAVs: the angular velocity at initial contact (IC), the maximum

angular velocity achieved during the first half of the deceleration phase (MAX), and

the difference between the maximum angular velocity and the angular velocity at

initial contact (DIF = MAX – IC). For the thigh segment, SAV(MAX) was calculated

as a maximum velocity in the sagittal and coronal planes and as a minimum velocity in

the transverse plane. For the shank segment, SAV(MAX) was calculated as a

minimum velocity in the sagittal plane and as a maximum velocity in the coronal and

transverse planes. In addition to the amplitude, the time point of the occurrence of

MAX was reported as a percentage of the stance duration. Finally, the difference

between the discrete SAV values of the two segments (thigh – shank) were calculated

for each parameter described above to estimate the relative angular velocity between

the thigh and shank segments.

In addition to the SAVs, the rotation of the segments in the coronal plane was

analyzed during the deceleration phase of landing because it is particularly relevant to

ACL injury (Hewett 2005) and because it might be associated with the knee abduction

moment. The maximum lateral angular displacement was measured because the thigh

and shank segments first rotate laterally (positive coronal SAV) after landing. To this

end, the coronal SAV was integrated over the deceleration phase, and the maximum

displacement was defined as the maximum value of the integration.

~76~

6.3.4. External knee moments

To measure the external knee moments, a marker-based motion capture system

(Qualisys Medical, Gothenburg, SE) with ten infrared cameras collecting at 120 Hz

and two force plates (Bertec, Columbus, OH) collecting at 1200 Hz were used. The

external moments were determined as previous described (Andriacchi 2004, Dowling

2010). Moments were normalized to percent bodyweight and height (%BW*Ht) to

allow for comparison between subjects.

The external knee abduction moment was analyzed during the stance phase

following the drop off the box. The initial contact (IC) and toe-off (TO) events were

defined as the time points when the vertical ground reaction force equaled 10N, and

the end of the deceleration was defined as the time point corresponding to the

minimum vertical height of the pelvis between IC and TO (Ford 2010). The discrete

values for the knee abduction moment were the same as the discrete values for the

SAV (IC, MAX and DIF). Subjects demonstrated two landing strategies for the knee

abduction moment; some subjects landed with primarily an abduction moment while

others landed with primarily an adduction moment. To preserve these strategies, the

average moment during the deceleration phase was calculated. If this average was an

abduction moment, then the MAX value was defined as the maximum abduction

moment; otherwise the MAX value was defined as the maximum adduction moment.

6.3.5. Data Analysis

The inter-subject variations of the SAVs were analyzed in terms of pattern and

amplitude for both leg segments during both jumping tasks. The coefficient of

multiple correlation (CMC) was used to assess the similarity between all the curves

obtained for each of the six SAVs from the two drop jump tasks (Kadaba 1989). For

the amplitude, at each discrete point (IC, MAX, DIF) the standard deviation (SD) over

all the jumps was calculated, and these SDs were compared to the total range of SAV

for the axis.

Paired Student t-tests were performed between the bilateral and unilateral SAV

discrete values and between the occurrences of the MAX angular velocity in order to

~77~

evaluate the sensitivity of SAV to distinguish between the landing mechanisms. The

association between the knee abduction moment and the SAVs at the discrete time

points was assessed with the Pearson correlation coefficient (R). This coefficient was

also used to characterize the association between the difference in knee abduction

moment between IC and TO (DIF) and the maximum coronal angular displacement of

the segments.

Finally, the receiver-operating characteristic (ROC) was plotted to assess the

ability of the SAVs to identify movements at higher risk for ACL injury (Zweig 1993).

The median value for each knee abduction moment discrete parameter was used to

divide the jumps into two groups, a higher risk and lower risk cohort. Based on the

literature, the group with the higher knee abduction moment was assumed to represent

the landings at higher risk for ACL injury. Finally, the area under the ROC plot was

calculated to characterize the discriminative performance of the SAVs (Zweig 1993).

All statistical tests were performed in MATLAB version R2010b (The Mathworks,

Natick, MA) and the significance level was set a priori to 0.05.

6.4. Results In general, the SAV curves were well defined movement patterns in all three

planes and displayed similar patterns for both jumping tasks (Figure 6-2 and Figure 6-

3). The CMC indicated high pattern similarities between subjects for the sagittal SAVs

and moderate to good similarities for the coronal and transverse SAVs (Table 6-1).

These coefficients agreed with the ranges of SAVs, which were higher for the sagittal

and transverse planes. Regarding the amplitude, the variations between subjects were

smallest for the sagittal plane with inter-subject SDs representing approximately 10%

of the range (Table 6-2). For the transverse and coronal planes, the SDs were

approximately 20% of the range.

Figure 6-2in sagitta

Initial conindicat

: Bilateral jal, coronal, ntact (IC) isted by white

jump angulaand transve

s indicated be star. Diffe

~78~

ar velocity cerse planes by black cirerence (DIF)

~

curves for s(axes are ac

rcle, and ma) is range b

shank and tccording to aximum stanetween IC a

high segmeFigure 6-1)nce (MAX) and MAX.

nts ). is

Figuresegments in

6-1). Iniindicat

e 6-3: Unilatn sagittal, coitial contactted by white

teral jump aoronal, andt (IC) indicae star. Diffe

~79~

angular veld transverse ated by blacerence (DIF)

~

ocity curves planes (axe

ck circle, ma) is range b

s for shank es are accoraximum staetween IC a

and thigh rding to Figance (MAX)and MAX.

ure )

~80~

Thigh Shank

Plane Jump CMC Range (°/sec)

SD (°/sec)

CMC Range (°/sec)

SD (°/sec)

Sagittal Bilateral 0.94 711.5 111.7 0.97 909.03 98.6

Unilateral 0.92 587.7 83.9 0.96 707.95 100.7

Coronal Bilateral 0.67 205.6 78.2 0.46 184.73 60.8 Unilateral 0.62 196.9 58.3 0.66 174.25 57.8

Transverse Bilateral 0.70 522.2 101.1 0.71 436.39 133.1 Unilateral 0.58 460.5 103.7 0.82 663.37 188.6

Table 6-1: Coefficients of multiple correlation (CMC) and ranges (SD) for the angular velocities of the shank and thigh segments in all three planes.

For all SAVs, the amplitude was close to or crossed zero °/sec at 50% of stance

phase, indicating that the segment stopped rotating in one direction and started rotating

in the other at this time point. During deceleration, the sagittal SAVs were positive for

the thigh and negative for the shank, indicating that the thigh is rotating backwards

and the shank is rotating forwards, resulting in knee flexion. In the coronal plane, the

SAVs were positive for both segments, suggesting that the thigh and the shank were

both rotating laterally. These similar directions of rotation didn't indicate a clear

overall rotation of the knee. The transverse SAVs were negative for the thigh and

positive for the shank, indicating external rotation for the thigh and internal rotation

for the shank and suggesting that the knee was rotating internally during deceleration.

The discrete parameters extracted at specific time points were able to

distinguish between the two types of jumping tasks (Table 6-2). There were significant

differences in the sagittal SAV at IC and MAX for the thigh, shank, and difference

between thigh and shank segments, for the DIF value of the shank, and for the timing

of MAX for the thigh segment. For the coronal plane, there were significant SAV

differences at IC and MAX for the thigh segment. In this plane, the timing of MAX

for the thigh segment was also significantly different between jumping tasks.

Regarding the transverse SAV, the differences between the jumping tasks were

significant for all the discrete values for the thigh, for IC and MAX for the shank, and

for MAX and DIF for the difference between the segments. Moreover, there were

significant differences in the occurrence of MAX for the shank.

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Thigh Shank Thigh - Shank

Time

Parameter Jump

Mean (°/sec)

SD Timepoint (% stance)

SD Mean (°/sec)

SD Timepoint (% stance)

SD Mean (°/sec)

SD

Sag

itta

l

Contact Bilateral 130.4** 53.1 -309.0** 73.5 439.4** 103.7

Unilateral 93.3** 58.4 -256.8** 77.4 350.1** 115.0

Max Stance Bilateral 301.9* 82.7 16.4** 6.2 -422.3** 58.6 4.1 1.4 724.1** 103.1

Unilateral 274.8* 61.2 13.2** 5.2 -325.9** 59.1 3.6 3.4 600.7** 82.8

Difference Bilateral 171.5 95.2 -113.3** 59.8 284.7 101.7

Unilateral 181.5 83.3 -69.1** 64.3 250.6 98.1

Cor

onal

Contact Bilateral -2.1* 28.0 11.3 45.5 -13.3 62.6

Unilateral 7.6* 29.8 6.9 38.7 0.7 46.3

Max Stance Bilateral 100.7* 49.1 15.7** 5.0 61.2 44.8 9.9 6.4 39.4 80.6

Unilateral 118.0* 43.7 10.1** 3.7 64.4 31.3 9.9 6.3 53.7 64.4

Difference Bilateral 102.7 46.3 50.0 33.1 52.8 64.7

Unilateral 110.4 52.8 57.4 36.1 53.0 57.3

Tra

nsve

rse

Contact Bilateral -28.7** 91.9 35.0** 85.1 -63.7 130.8

Unilateral 104.5** 85.9 173.1** 95.6 -68.6 111.1

Max Stance Bilateral -279.7** 82.5 13.6 5.0 195.6** 73.9 8.5** 4.8 -475.3** 106.5

Unilateral -224.9** 74.6 13.0 5.1 326.1** 101.3 4.7** 2.5 -551.0** 120.8

Difference Bilateral -251.0** 126.4 160.6 85.0 -411.6** 159.0

Unilateral -329.3** 117.5 153.1 91.2 -482.4** 137.0

Table 6-2: Angular velocity parameters measured at specific time points. *p < 0.01, **p < 0.001: significant difference between jump task

~82~

The thigh and shank coronal SAVs were significantly correlated with the knee

abduction moment for most of the discrete measurements and for all of the maximum

coronal angular displacements during the two jumping tasks (Table 6-3). The

significant correlations were always positive for the thigh segment, negative for the

shank segment, and positive for the thigh – shank measurement, which agreed with the

direction of rotation (Figure 6-4). For the significant correlations, the area under the

ROC plots were between 0.61 and 0.75 for the thigh segment, between 0.59 and 0.72

for the shank segment, and between 0.57 and 0.78 for the thigh – shank measurement

at identifying the jumps that were considered as being at higher risk for ACL injury

according to knee abduction moment values (Table 6-3).

Thigh Shank Thigh – Shank

Time

Parameter Jump R ROC R ROC R ROC

Ang

ular

Vel

ocit

y Contact Bilateral -0.04 0.54 -0.08 0.59 0.04 0.58

Unilateral 0.00 0.49 0.03 0.52 -0.02 0.55

Max Stance Bilateral 0.32** 0.61 -0.16 0.52 0.28** 0.57

Unilateral 0.38** 0.71 -0.34** 0.65 0.43** 0.72

Difference Bilateral 0.46** 0.74 -0.35** 0.66 0.51** 0.78

Unilateral 0.25* 0.61 -0.20* 0.59 0.35** 0.69

Coronal Angular Displacement

Bilateral 0.26* 0.70 -0.20* 0.60 0.26* 0.68

Unilateral 0.46** 0.75 -0.41** 0.72 0.47** 0.75

Table 6-3: Correlation (R) between knee abduction moment and coronal plane angular velocity, as well as receiver operating curves (ROC).

*p < 0.01, **p < 0.001: significant correlation between angular velocity parameter and knee abduction moment

Figure 6-knee ab

abduction moment, C

6.5. Di

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landings. A

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ump landin

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AVs indicat

~83~

elationship positive thighank SAV t

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~84~

planes (flexion and internal rotation angles). No clear tendency was apparent from the

coronal SAVs, which is also supported in the literature as various studies have

reported both knee abduction and adduction angles during the deceleration phase

(Ford 2003; Ford 2010; Hewett 2005a; Kiriyama 2009; McLean 2007; Pappas 2007;

Russell 2006; Yu 2005). Contrary to joint angles, SAVs provide an analysis of the

individual movements of the thigh and shank segments as well as their relative

movement (coordination). Moreover, SAVs characterize angular dynamic movement

instead of static movement (velocity vs. displacement). For example, this study

showed that the shank segment reached its maximum angular velocity sooner than the

thigh segment in the sagittal and coronal planes for both jump tasks, showing that the

shank segment stabilized first in these two planes. This study also reported that the

thigh and shank coronal MAXs occur simultaneously and later during stance for the

shank segment. These results support using SAVs as a complement to knee angles in

the understanding of the neuromuscular control and injury mechanisms during jump

landing tasks.

Several differences in the SAVs were noticed between the bilateral and

unilateral landing mechanics (Table 6-2). Overall, out of the eleven parameters (nine

amplitudes and two timings) extracted in each plane, between four and nine were

significantly different between jumping tasks, which is comparable to previously

reported results for knee angles (Chapter 5). In general, the changes in the SAV

amplitudes between the jumping tasks were smaller than the inter-subject variations,

suggesting that SAV is a reliable characterization of landing mechanics. The timing of

the maximum values of the SAVs also differed between jumping tasks. During the

unilateral jumps, the maximum SAV (MAX) occurred sooner in stance for the thigh

segment in all three planes as well as for the shank segment in the transverse plane.

These differences are most likely a result of faster stabilization adjustments that are

necessary in order for the subjects to successfully complete the unilateral landing. This

observation illustrates the importance of understanding the dynamic landing

mechanics like SAV because the knee is most vulnerable during the early stance phase

(Boden 2000; McNair 1990; Olsen 2004). Based on these differences, SAV could

~85~

certainly be used to identify subjects with abnormal movements and might be able to

determine if a specific subject with abnormal movement is more at risk for a future

injury. However, further investigation is necessary to determine if abnormal SAVs are

correlated to an increased incidence of injury.

Furthermore, this study tested the hypothesis that there is a relationship

between the coronal SAV and knee abduction moment. There were two motivations

behind this test. First, an association with a known risk factor such as knee abduction

moment would support the idea that SAV could be an important parameter in the

identification of landing movements at higher risk for ACL injury. Second, an

association between these two metrics would suggest that SAV could be used to

predict knee abduction moment. This association would be highly beneficial in terms

of screening or feedback for risk of ACL injury, because measuring SAV is simple

and can be performed anywhere. As hypothesized, the coronal SAVs at MAX and DIF

were significantly correlated with the knee abduction moment (Table 6-3).

Furthermore, the coronal angular displacement was also significantly correlated with

knee abduction moment. The correlations were not strong, but this is to be expected

when two complex metrics are tested only for linearity. However, it is important to

note that the sign of the correlations agreed with the biomechanics, as illustrated in

Figure 6-4. Physically, when the thigh segment has a positive coronal angular velocity,

it is rotating in the direction that will increase the abduction moment, and therefore the

correlation with knee abduction moment should be positive. On the other hand, a

positive shank angular velocity will decrease the abduction moment. Finally, a

positive difference between the thigh and shank SAVs indicates that the knee is

abducting, which corresponds to an increase in knee abduction moment.

Given these associations, the potential of the coronal SAV to differentiate

between subjects at higher risk for ACL injury (based on the knee abduction moment)

was evaluated (Table 6-3) by receiver operating characteristic (ROC) plots. On

average, the area under the ROC plots for the significant correlations was 0.69 for the

thigh segment, 0.64 for the shank segment, and 0.70 for the difference between thigh

and shank segments. These values suggest that the coronal SAV can identify

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movements classified as higher risk, but with limited sensitivity and specificity. It is

important to note that the two cohorts (lower and higher risk) were not determined

based on a knee abductiom moment threshold obtained from previous research, such

as in studies by Myer et al. (Myer 2007, Myer 2010a). Therefore, the present ROC

values constitute only a first evaluation of the discriminative potential of SAV, and

better results could be expected if a threshold obtained through a prospective study

was used. These results confirmed that SAV could be a useful parameter to analyze

jump landing movements, and that coronal SAV is associated with knee abduction

moment. This association with knee abduction moment is important because research

has shown that knee abduction moment can accurately predict future ACL injury with

high sensitivity and specificity (Hewett 2005a). Furthermore, simpler methods to

predict knee abduction moment (and by extension risk for ACL injury) have been

investigated since knee abduction moment is complex to measure. (Myer 2010a, Myer

2010b, Myer 2010c). Given the association between knee abduction moment and

SAV, it is possible that a multifaceted model relying on the biomechanical parameters

suggested by Myer et al. (Myer 2010a, Myer 2010b, Myer 2010c) but with the

addition of the coronal SAV will result in a highly sensitive and specific model of

knee abduction moment and therefore will be able to accurately predict risk for a

future ACL injury.

Although joint angles and moments are the metrics that are most widely used

to describe the biomechanics of the knee, segment angular velocity has also been

shown to be important (Cham 2002; Damiano 2006; Favre 2010; Mills 2001; Radin

1991; Salarian 2004; Yu 2006). One explanation for the limited use of SAV in the past

might be because it is highly sensitive to measurement errors when it is derived from

camera-based motion capture systems. However, now SAV can be reliably and easily

measured using inertial measurement units without spatial or temporal constraints

because these units include a triaxial gyroscope which measures the angular velocity

directly. Furthermore, functional calibration procedures have been developed to align

the gyroscope axes to the bone anatomical frame of the thigh and shank segments

(Favre 2009), making the measurements independent of the sensors’ placement on the

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segments. Additionally, the computing requirements to process the angular velocity

are minimal, making SAV very attractive as a real time measurement.

6.6. Conclusions This study reported the angular velocity of the thigh and shank segments

during bilateral and unilateral drop jumps for the first time. It showed that lower limb

SAV was consistent between subjects and therefore could be reduced down to discrete

values to describe the landing movement and compare landing mechanics during drop

jump tasks. Additionally, these results showed that there is an association between the

coronal SAV and knee abduction moment, and that the coronal SAV can differentiate

between subjects at higher risk for ACL injury. In conclusion, this study demonstrated

that SAV, which is simple to measure with inertial measurement units, is a valuable

metric to describe landing biomechanics.

6.7. Acknowledgments This work was supported by an NSF graduate fellowship, the Palo Alto VA,

and the Stanford Center on Longevity. Thanks to Dr. Kamiar Aminian from EPFL for

his assistance.

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77 Real Time Inertial-Based Feedback Can Reduce Risk for ACL Injury During Jump Landings

7.1. Overview Recent studies have shown that the incidence of ACL injury can be decreased

through the use of intervention programs, but the quality of the feedback provided to

the participants in these programs can vary depending on the skill of the observer. The

objective for this investigation was to determine if an independent inertial-based

system can be used to modify jump landing mechanics in order to decrease the risk for

ACL injury by providing real-time feedback based on known kinematic and kinetic

injury risk factors. Seventeen subjects (7 male) conducted drop jump tasks while

wearing an inertial-based measurement system that provided feedback on the relative

risk of the task in terms of the knee flexion angle, trunk lean, and thigh coronal

velocity. The subjects conducted a baseline session with no landing instructions, then

a training session where they received feedback from the system, and finally a follow-

up session where they maintained the jumping technique learned during the training

session. The baseline and follow-up sessions were then compared. The subjects

increased their knee flexion angle (16.2°), and their trunk lean after the training (17.4).

The subjects also altered the thigh coronal angular velocity by 29.4°/sec and reduced

their knee abduction moment by 0.5 %BW*Ht. There was a significant correlation (R2

= 0.55) between the change in the thigh coronal angular velocity and the change in the

knee abduction moment. The subjects reduced their risk for ACL injury after training

~89~

with the system because there were significant positive changes in the known

kinematic and kinetic injury risk factors. This study suggests that an inertial-based

system could be used for interventional training aimed at reducing the risk for ACL

injury.

7.2. Introduction As described in Chapter 1, the anterior cruciate ligament (ACL) is the most

commonly injured ligament of the knee. As such, researchers have developed

intervention programs that can successfully decrease the incidence of ACL injury

(Chapter 2), but compliance rates can be as low as 28% (Myklebust 2003).

Furthermore, either an instructor or a physical therapist must be present to coach the

participants (Alentorn-Geli 2009b; Brophy 2010; Hewett 2006b; Renstrom 2008;

Silvers 2007). An independent and quantitative feedback system could greatly

improve these intervention programs by allowing the subjects to conduct the training

sessions on their own while still receiving consistent instructions.

Many of the successful intervention programs emphasize proper jump landing

technique. Furthermore, specific kinematic and kinetic parameters, such as a small

knee flexion angle, small trunk flexion angle, and large knee abduction moment, have

been shown to increase the risk for ACL injury during a jump landing (Chapter 2).

Measuring these parameters requires a complex setup (e.g., gait laboratory) as well as

a substantial amount of time to prepare the subject and process the data.

In the past decade, inertial-based systems have been developed to simplify the

measurement of human movement and monitor subjects in their natural environment

(Chapter 2). One such system has been recently validated; this system was primarily

focused on analyzing the knee flexion angle and trunk lean during a drop jump task

(Chapter 5). Chapter 6 also described an association between the thigh coronal angular

velocity and the knee abduction moment during a drop jump task. Altogether, the

literature suggests that an inertial-based system could be used to assist subjects during

the training sessions of intervention programs by providing quantitative feedback

~90~

(Crowell 2011). However, it is currently unknown if subjects can respond to feedback

provided by an inertial-based system during a jumping task.

The overall objective for this study was to determine if an independent

inertial-based system can be used to modify jump landing mechanics in order to

decrease the risk for ACL injury. Quantitative real-time feedback was provided for the

knee flexion angle, trunk lean, and thigh coronal angular velocity. The first specific

hypothesis tested in this study was that subjects could respond to the real-time

feedback within a short, same-day training period. The second specific hypothesis was

that by decreasing their thigh coronal angular velocity during the deceleration phase of

the landing, the subjects would also decrease their knee abduction moment.

7.3. Methods 7.3.1. Subjects

Seventeen subjects (7 male and 10 female) with an average age of 27.5 ± 2.9

years and BMI of 22.8 ± 2.3 were selected for this study. All were regular participants

in sports involving jumping maneuvers at the recreational level. Subjects with

previous lower limb musculoskeletal injuries requiring surgery or any current

symptoms of pain or injury were excluded. This study was approved by the

Institutional Review Board and informed written consent was obtained from all

subjects prior to data collection. The subjects were unaware of the goals of the study

prior to the start of the testing session.

7.3.2. Jump Task

The jump task for this study was a bilateral support drop jump maneuver in a

gait laboratory (Ford 2003; Noyes 2005). For this task, each subject dropped off a 36

cm box, landed with both feet on the ground, and then immediately performed a

maximum height vertical jump. The landing directly after the drop from the box was

used for the analysis. The jump was considered acceptable if the subject dropped off

~91~

the box with both feet at the same time and fully impacted a force plate embedded in

the ground with their right-side foot.

7.3.3. Feedback

7.3.3.1. Hardware

The feedback system consisted of three small inertial measurement units

(Physilog®, BioAGM, CH) affixed on the chest, thigh, and shank segment

respectively (Chapter 5). These units were connected to a computer that recorded the

signal from the inertial sensors at 240 Hz during the jump task. Using custom

software, the knee flexion angle, trunk lean, coronal thigh angular velocity, and

vertical jump height were calculated immediately after the subject completed the jump

trial (Chapter 5; Chapter 6). The technical details of this system, as well as its

validation for drop jump analysis, have been presented elsewhere (Chapter 5; Chapter

6; Favre 2009; Favre 2008; Favre 2006). Finally, a projector was used to display the

results of the jump analysis. It took less than 10 minutes to place this system on a

subject and less than five seconds to analyze a jump.

7.3.3.2. Parameters

The feedback consisted of three kinematic parameters (knee flexion angle,

trunk lean, and coronal thigh angular velocity) plus the jump height. One characteristic

feature previously identified as being associated with ACL injury was extracted from

each kinematic parameter (Chapter 5; Chapter 6). For the knee flexion angle and trunk

lean, the maximum values achieved during stance were chosen as feedback parameters

because they have been suggested as risk factors for ACL injury (Blackburn 2008;

Blackburn 2009; Griffin 2000, Hewett 2005a; Huston 2001; Yu 2005) and are

common components of intervention programs (Chappell 2007; Chappell 2008;

Herman 2009; Hewett 1999; Mandelbaum 2005; Myer 2007; Myer 2005; Myklebust

2003; Olsen 2005; Pollard 2006). For the thigh coronal angular velocity, the first

maximum (inward) peak during stance was selected because it is correlated to the knee

abduction moment (Chapter 6), which is a strong predictor of ACL injury risk (Ford

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2010; Hewett 2005a; McLean 2007; Renstrom 2008). Additionally, the jump height

was included in order to ensure that the modifications of the jump landing mechanics

did not adversely affect the performance of the jump.

7.3.3.3. Relative Risk

Based on the literature described in the introduction, a direction associated

with a higher risk for injury was determined for the three kinematic feedback

parameters (i.e., knee extension, backward trunk lean and inward thigh coronal

angular velocity). Then, the actual target values for the feedback were determined

based on previous research on healthy subjects conducting drop jumps that used the

same inertial-based system (Chapter 5; Chapter 6). Knee flexion angle and trunk lean

have been widely documented in the context of ACL injury; therefore relatively small

risk ranges were defined for these two parameters and the subjects were instructed to

be to be within those ranges. The risk ranges, [88°; 120°] for the knee flexion angle

and [25°; 60°] for the trunk lean, corresponded to the upper half [median; maximum]

of the data previously collected with healthy subjects (Chapter 5). No target range was

defined for the thigh coronal angular velocity because a risk threshold has never been

reported for this parameter nor has it been used in an intervention program. Instead,

the participants were instructed to land in a neutral manner (i.e., with the first peak of

the thigh coronal angular velocity equal to 0°/sec).

7.3.4. Experimental Design

The experimental protocol consisted of seven parts (Figure 7-1). During the

preparation, the feedback system and the reflective markers for an optoelectronic

system (Andriacchi, 1998) were placed on the subjects. The subjects then performed a

short warm-up consisting of light jogging and/or squatting. When the subjects felt

ready, calibration procedures were performed for the feedback (Chapter 5; Favre

2009; Favre 2008) and optoelectronic (Andriacchi 1998; della Croce 1999) systems.

After that, the jumping task was explained to the subjects, and they were allowed to

practice until they felt confident with the task. At this point, the subjects conducted a

~93~

baseline testing session consisting of three drop jumps. For the baseline session, no

landing instructions were provided and the subjects were not told what the feedback

parameters would be. Following the baseline testing, the subjects completed a training

session of 15 to 20 jumps within approximately 30 minutes where they received

feedback on their jumping technique. Immediately after the training session, the

subjects conducted a follow-up session also consisting of three drop jumps. For the

follow-up testing, the subjects were asked to maintain the jumping technique that they

learned during the training session. The subjects also had the opportunity to repeat a

jump during this session if they felt that they did not successfully accomplish the

movement modification.

Figure 7-1Vertical arrow

: Experimentaws indicate wh

~94~

l protocol for ehen feedback wa

entire testing seas given to the

ession. subjects.

~95~

7.3.4.1. Training Session

Once the baseline testing was complete, the average values over the three

baseline jumps were calculated for each feedback parameter. These averages were

then projected onto the wall in front of the subject (Figure 7-2) and the four feedback

parameters were verbally explained to the subject using a standardized speech. For the

knee flexion angle and trunk lean, the low risk range was shaded green. The subjects

were also given a standardized set of movement modifications for each parameter that

would reduce their risk of injury (Table 7-1) and were told they would have between

15 and 20 jumps to incorporate the modifications into their jumping technique. The

subjects were then instructed to modify their landing mechanics in order, starting with

the knee flexion angle (if necessary), then the trunk lean (if necessary), and finally the

thigh coronal angular velocity (if necessary). Regarding the jump height, the subjects

were instructed to maintain their baseline height for all the subsequent jumping trials.

After each jump, the display was immediately updated to add the results of the latest

jump to the subject’s training history (Figure 7-2). The examiners assisted the subjects

during the training by indicating when to move on to the next feedback parameter and

by suggesting changes in the landing technique using the standardized set of

movement modifications. When the subjects achieved a jumping technique that

optimized all the feedback parameters, or when they reached 20 training jumps, they

were asked to maintain that technique for the follow-up trials.

Parameter Standardized Movement Modifications

Knee Flexion Angle

Bend knees more during landing

Land softly

Trunk Lean Bend torso more during landing

Coil like a spring

Thigh Coronal Angular Velocity

Push knees outward at the beginning of landing

Increase toe-out angle

Move feet closer together during landing

Table 7-1: Standardized set of movement modifications for training session

Figure 7-2:indicatevalues, a

7.3.5. K

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~96~

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~97~

force plate (Bertec, Columbus, OH) collecting at 1200 Hz. The point cluster technique

was used to track the orientation of the shank and foot frames (Andriacchi 1998), and

the knee abduction moment was calculated using an inverse dynamic approach

(Andriacchi 2004, Dowling 2010). The subjects demonstrated two landing strategies

for the knee abduction moment: some subjects landed with primarily an abduction

moment while others landed with primarily an adduction moment. To preserve these

strategies, the average moment during the deceleration phase of the landing was

calculated. If this average was positive (mainly in abduction), then the maximum

value (abduction peak) was reported; otherwise the minimum value (adduction peak)

was reported. To allow for comparison between subjects, the knee abduction moment

was normalized to percent bodyweight and height (%BW*Ht).

7.3.6. Statistical Analysis

For each of the five parameters considered in this study (knee flexion angle,

trunk lean, thigh coronal angular velocity, knee abduction moment, and jump height)

during both the baseline and the follow-up sessions, the values from the three jumps

were averaged in order to have one mean value per subject per session. Paired Student

t-tests (baseline vs. follow-up) were used to evaluate the effects of the training.

Additionally, the association between the change in the thigh coronal angular velocity

from baseline to follow-up and the change in the knee abduction moment from

baseline to follow-up was assessed with the Pearson correlation coefficient (R). All

statistical tests were performed in MATLAB version R2010b (The Mathworks,

Natick, MA) and the significance level was set a priori to 0.05.

7.4. Results Within a 20 jump training session, all of the subjects were able to respond to

the feedback from the inertial-based system in terms of the knee flexion angle and the

trunk lean, and most of the subjects were also able to change the amplitude of their

thigh coronal angular velocity. However for some subjects, more than one training

session would have been necessary to obtain a landing technique that satisfied all three

~98~

parameters. The feedback history for a full test (baseline to follow-up) is shown for a

typical subject in Figure 7-2. In terms of the maximum knee flexion angle, at baseline

9 subjects were outside the low risk range, and at follow-up all subjects were inside

the pre-defined range (Table 7-2, Figure 7-3). All but one subject increased their knee

flexion angle during the training (average change: 16.2°, p < 0.001), and the one

subject that did not had a relatively high baseline value (104°). The results were

similar for the maximum trunk lean. At baseline, 10 subjects were outside the low risk

range, and at follow-up all subjects were inside the range (Table 7-2, Figure 7-3). All

17 subjects increased their trunk lean during the training (average change: 17.4°, p <

0.001). In terms of thigh coronal angular velocity, at baseline 16 subjects had a

positive value (indicating an inward movement of the thigh after initial contact) and

one subject had a negative value (indicating an outward movement of the thigh after

initial contact). After training, 13 subjects landed with a more neutral thigh coronal

angular velocity, and 4 subjects were not able to complete the third modification.

Overall, the subjects altered the first peak of the thigh coronal angular velocity by

29.4°/sec (p < 0.01) (Table 7-3, Figure 7-3). The subjects also maintained the same

jump height from baseline to follow-up (p = 0.6), and the average change in jump

height was 0.5 cm.

Number at Risk

Average SD

Kne

e F

lexi

on

Ang

le (

°)

Baseline 9 88.8** 8.0

Follow-Up 0 105.0** 5.6

Change -9 16.2 10.0

Tru

nk

Lea

n (°

) Baseline 10 23.6** 8.0

Follow-Up 0 41.0** 3.8

Change -10 17.4 8.4

Jum

p H

eigh

t (c

m) Baseline 35.2 6.4

Follow-Up 35.8 7.0

Change 0.5 2.7

Table 7-2: Knee flexion angle, trunk lean, and jump height at baseline and follow-up. Number at risk indicates subjects outside the low risk ranges.

**p < 0.001: Difference between baseline and follow-up

~99~

Average SD

Thigh Coronal Angular Velocity (°/sec)

Baseline 67.7* 49.7

Follow-Up 47.6* 40.5

Change 29.4 31.6

Kne

e A

bduc

tion

M

omen

t (%

BW

*Ht)

ABD Baseline

Baseline 2.0^ 0.9

Follow-Up 1.2^ 1.5

Change -0.8 1.0

ADD Baseline

Baseline -1.7 0.8

Follow-Up -2.0 1.0

Change -0.3 1.3

Table 7-3: Thigh coronal angular velocity and knee abduction moment for both systems at baseline and follow-up. For thigh coronal angular velocity, change calculated as the average difference between baseline and follow-up (absolute

value). Knee abduction moment split into at-risk (ABD Baseline) and not-at-risk (ADD Baseline) cohorts.

*p < 0.01: Difference between baseline and follow-up ^p = 0.06: Trend to significance between baseline and follow-up

FFigure 7-3: Chaange in knee flefrom baseline

exion angle, truto follow-up. G

~100~

unk lean, and thGreen shading i

high coronal anindicates low ri

ngular velocityisk range.

y by subject

~101~

Regarding the peak knee abduction moment, the average change for all the

subjects was -0.5 %BW*Ht (p < 0.001). For further analysis, the subjects were split

into two cohorts based on their baseline values because previous work (Hewett 2005a)

has shown that only an abduction moment increases the risk of ACL injury. At

baseline, 8 subjects had an abduction (positive) moment and were classified as "at-

risk", whereas 9 subjects had an adduction (negative) moment and were classified as

"not-at-risk" (Figure 7-4). For the at-risk cohort, 6 subjects decreased their knee

abduction moment during the training (-1.2 %BW*Ht) while 2 increased their knee

abduction moment (0.4 %BW*Ht). For the entire at-risk cohort, the average change

was -0.8 BW*Ht (trend to significance: p = 0.06) (Table 7-3, Figure 7-4). Moreover, 2

of the subjects in the baseline at-risk cohort had an adduction (not-at-risk) moment

after the training. None of the subjects in the baseline not-at-risk cohort had an

abduction (at-risk) moment after the training, and the average change for this cohort

was not statistically significant (Figure 7-4). Finally, a significant correlation (R2 =

0.55, p < 0.001) was obtained between the change in the knee abduction moment and

the change in the thigh coronal angular velocity (Figure 7-5).

Figure 7follow-up

positive (a

7-4: Changep, split into abduction) p

had a neG

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~102~

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~103~

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~104~

important to note that the subjects were instructed to modify their landing mechanics

one parameter at a time and that the thigh coronal angular velocity was the final

parameter. Since the majority of the subjects were able to decrease their thigh coronal

angular velocity, this result also suggests that some subjects might need more than one

training session.

At follow-up, the subjects had significantly reduced their risk for ACL injury

in term of the kinematic risk factors. After training, the subjects increased both their

maximum knee flexion angle and their maximum trunk lean during stance. The 16.2°

increase obtained for the knee flexion angle is comparable to previous intervention

programs consisting of a single training session. Mizner et al. (Mizner 2008) reported

a change of 11.3° for female athletes instructed to increase their knee flexion angle

during a drop jump landing. Another study that investigated different combinations of

feedback during a vertical jumping task reported changes in knee flexion angle

between 27° and 40° (Oñate 2005). Although no study has directly reported the

change in trunk lean after an intervention, the 17.4° increase observed in this study

agrees with the change reported by Blackburn et al. (Blackburn 2007) for trunk flexion

angle during a controlled drop jump landing task. Furthermore, it is important to note

that in this study the amplitude of change for each kinematic parameter was driven by

the low risk range; it is assumed that with enough training, any subject could land with

an exact amplitude of knee flexion angle and trunk lean.

As hypothesized, by decreasing their thigh coronal angular velocity, the

subjects also decreased their knee abduction moment. In fact, there was a strong

association between the change in these parameters from baseline to follow-up (R2 =

0.55). This result clearly indicates that the thigh coronal angular velocity could be

related to ACL injury. Whereas Chapter 6 suggested that the thigh coronal angular

velocity could be used to differentiate subjects with high or low knee abduction

moment, this study showed that the thigh coronal angular velocity could be used to

estimate the change in the knee abduction moment as a result of an intervention. This

is important because the knee abduction moment is a complex parameter to measure

and therefore cannot be used in large-scale intervention programs. While the thigh

~105~

coronal angular velocity alone cannot accurately predict the actual value of the knee

abduction moment, it can provide a strong overall assessment of the effect of a

movement alteration (e.g., upper body positioning or foot stance) on the knee

abduction moment and therefore on the risk for ACL injury. A more complex model

needs to be developed in order to predict the actual value of the knee abduction

moment from the thigh coronal angular velocity (Meyer 2010a).

The decrease in the knee abduction moment between baseline and follow-up

also suggested that the feedback system successfully guided the subjects to decrease

their risk for ACL injury during the training (Hewett 2005a). The average decrease of

0.8 %BW*Ht for the at-risk cohort is comparable to the results from previous

intervention programs. Mizner et al. (Mizner 2007) reported a 0.65 %BW*Ht

reduction in the knee abduction moment for female athletes instructed to land softly

and avoid knee valgus during a drop jump landing. In another study, female athletes

considered at higher risk for ACL injury displayed a decrease of 0.5%BW*Ht during a

drop jump landing after a 7 week neuromuscular training program (Myer 2007).

This study demonstrated that a real-time inertial-based system could be used

for interventional training aimed at reducing the risk of ACL injury. This system

provides immediate feedback as to which movement modifications and jumping

strategies are most effective for the individual subject, and therefore the movement

modifications can be customized for each subject. Furthermore, the system provides

quantitative feedback that can be saved to monitor the subject’s progress or to

compare subjects. Another benefit of this system is that it is independent and does not

require a trained observer to administer the feedback. In terms of user-friendliness, the

system used in this study consisted of only three small inertial measurement units and

a computer, required just a few minutes to set up, and automatically analyzed the data

after each jump. Therefore it would be ideal for decentralized use, such as in a home

setting. This system could increase the compliance rates of interventional training

because the participant would not have to travel to receive the training. To improve

the usability of this system in a home setting, future iterations might not require a

~106~

computer for the analysis or could suggest movement modifications in the form of a

video game.

One limitation of this study was that it included only a small number of healthy

subjects. Further research should be conducted with a larger cohort of potentially at-

risk subjects, such as young female athletes, to determine how these subjects respond

to training with the feedback system. Furthermore, the target values for the

intervention (low risk ranges for the knee flexion angle and trunk lean, neutral landing

for the thigh coronal angular velocity) were based on previous research conducted

with healthy subjects. While these target values were appropriate for the healthy

population enrolled in this study, a full prospective study using the inertial-based

system is necessary to determine the target values that correspond to actual risk for

ACL injury.

In this study only three parameters were used for the feedback because it was

anticipated that the subjects would not be able to modify more than three parameters

during a single training session. However, there are many other known ACL injury

risk factors that could be included in the feedback in order to improve the intervention.

This inertial-based system can also measure knee angles in the coronal and transverse

plane (Favre 2009) as well as velocity in all three planes (Favre 2010) without

modifications to the hardware or the data collection procedure. However, before

increasing the number of feedback parameters, an overall risk assessment “score”

should be developed in order to combine the disparate risk factors. This type of overall

score would help the subjects to understand their level of risk in a concise manner.

Additionally, this score could be used to classify the feedback parameters according to

their importance. One of the difficulties in developing an overall risk score is that a

large amount of data from many subjects is needed to build a reliable risk assessment

model. However, the system used in this study can also effectively collect quantitative

data and therefore could be used to develop an overall risk assessment score. Finally,

the inertial-based system is a simple system that can quickly and easily collect data

from many subjects in a natural environment, and therefore it would be an ideal

system to prospectively screen subjects for ACL injury risk.

~107~

7.6. Conclusions This investigation determined that an independent inertial-based system can be

used to modify jump landing mechanics in order to decrease the risk for ACL injury.

The subjects could effectively respond to feedback from this system in a short training

session. Furthermore, the subjects reduced their risk for ACL injury after training with

this system because there were significant increases in the maximum knee flexion

angle and the maximum trunk lean. The subjects also reduced their risk for injury by

decreasing their thigh coronal angular velocity, which was correlated with a decrease

in their knee abduction moment. This study suggests that an inertial-based system

could be used for interventional training aimed at reducing the risk for ACL injury.

This system is independent, simple to set up and use, and automatically provides

quantitative feedback to the subject. Therefore it would be an ideal system for use in a

home setting or to prospectively screen subjects for ACL injury risk.

7.7. Acknowledgments This work was supported by an NSF graduate fellowship, the Palo Alto VA,

and the Stanford Center on Longevity. Thanks to Dr. Kamiar Aminian from EPFL for

his assistance.

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88 Summary

8.1. Overall Conclusions

The overall goal of this dissertation was to use novel motion analysis systems

to investigate the underlying mechanisms that cause an anterior cruciate ligament

(ACL) injury and then to explore movement modification methods that might prevent

ACL injuries from occurring. The results from multiple experimental studies that used

two novel motion analysis systems have been presented in the preceding chapters.

These results add to the understanding of the ACL injury mechanism and also suggest

potential preventative methods that could decrease the overall incidence of ACL

injury.

Chapter 3 analyzed how subjects change their movement strategies for shoe-

surface conditions with a high coefficient of friction relative to a low friction condition

and how these changes in movement strategies affected their risk for ACL injury. This

study found that a high COF condition was associated with a lower knee flexion angle,

higher external knee flexion and knee abduction moments, and greater medial distance

of the COM from the support limb, all of which suggest an increased risk for ACL

injury. The primary conclusions were that increasing the COF of the shoe-surface

condition will change a subject’s movement strategies during a sidestep cutting task in

specific ways that may increase the risk of ACL injury, providing a biomechanical

basis for the increased incidence of ACL injuries on high friction surfaces.

Chapter 4 investigated how increasing running speed prior to a single limb

landing combined with increased floor friction alters a subject’s movement as well as

how these alterations are different between males and females. This study found that

the high speed, high friction condition resulted in an increased knee flexion angle,

increased knee flexion, adduction, and internal rotation moments, and a greater medial

~109~

and posterior distance of the center of mass from the support limb. Furthermore, the

differing adaptations to the high friction surface observed at different speeds suggest

that the biomechanical causes for the higher incidence of ACL injury on high friction

surfaces change based on the speed of the maneuver. In terms of gender, for every

condition females exhibited significantly lower knee flexion angles than their male

counterparts and showed a trend towards an increased knee abduction angle,

suggesting that they are more at risk for ACL injury during all the conditions.

Chapter 5 explained the development and assessment of a wearable inertial-

based system to measure jumping tasks in terms of temporal event detection, jump

height, and knee angles. The wearable system proposed in this study extended the

functionality of inertial-based systems to analyze jumps. It accurately detected crucial

temporal events and measured total jump height with a precision comparable to

dedicated optical devices. Additionally, the proposed system measured the knee

flexion and the trunk lean, and demonstrated good concurrent validity and

discriminative performance in terms of the known risk factors for ACL injury.

Chapter 6 described the characterization of the thigh and shank angular

velocity during a jump landing and the association between coronal angular velocity

and knee abduction moment. This study reported the angular velocity of the thigh and

shank segments during bilateral and unilateral drop jumps for the first time. It showed

that lower limb SAV was consistent between subjects and therefore could be reduced

down to discrete values to describe the landing movement and compare landing

mechanics during drop jump tasks. Additionally, these results showed that there is an

association between the coronal SAV and knee abduction moment, and that the

coronal SAV can differentiate between subjects at higher risk for ACL injury.

Chapter 7 determined that an independent inertial-based system can be used to

modify jump landing mechanics in order to decrease the risk for ACL injury. The

subjects could effectively respond to feedback from this system in a short training

session. Furthermore, the subjects reduced their risk for ACL injury after training with

this system because there were significant increases in the maximum knee flexion

angle and the maximum trunk lean. The subjects also reduced their risk for injury by

~110~

decreasing their thigh coronal angular velocity, which was correlated with a decrease

in their knee abduction moment. This study suggests that an inertial-based system

could be used for interventional training aimed at reducing the risk for ACL injury.

8.2. Contributions

This thesis made significant contributions to the scientific knowledge on ACL

injuries and ACL injury prevention. Chapters 3 and 4 expanded the understanding of

the ACL injury mechanism on different coefficient of friction surfaces. This work

illustrated how subjects change their movement strategies (in terms of specific

biomechanical variables) as a result of changing the surface coefficient of friction

during a run to cut task. Furthermore, this work provided a biomechanical basis for the

increased incidence of ACL injuries on high friction surfaces. This thesis also

illuminated the biomechanical differences between male and female athletes during

cutting as a result of surface friction, and provided additional evidence as to why

females are more at risk for ACL injury.

Chapter 5 characterized the use of a wearable inertial-based motion analysis

system during a jumping task. This study was the first investigation to describe the use

of this type of motion analysis system during jumping tasks, specifically in terms of

temporal event detection, jump height, and sagittal plane angles. Furthermore, the

results showed that the proposed system could be used to determine increased risk for

ACL injury, suggesting that this simple system could be a promising tool for

conducting risk screening in a natural environment.

Chapter 6 established thigh and shank angular velocity as important parameters

to analyze jump landing mechanics. This work showed that angular velocity had a

distinctive pattern that characterized the dynamics of the movement and added new

information about the movement of the lower limbs. Furthermore, this study was the

first to show an association between the thigh angular velocity in the coronal plane

and the knee abduction moment. This association suggests that angular velocity could

be related to the risk of ACL injury because it is correlated with an important known

~111~

ACL injury risk factor. This study further supports the importance of measuring

angular velocity during a jump landing movement.

Chapter 7 demonstrated that an independent inertial-based system can be used

to modify jump landing mechanics. This system efficiently reduced the risk for ACL

injury by providing real-time feedback in terms of known kinematic and kinetic risk

factors. This was one of the first studies to train subjects by using quantitative

feedback primarily from an inertial-based system, showing that a simple feedback

system could be used outside of a research environment. Furthermore, this study

demonstrated an association between the change in the thigh coronal angular velocity

and the change in the knee abduction moment. These results show that thigh coronal

angular velocity can provide a strong overall assessment of the effect of a movement

alteration on the knee abduction moment and therefore on the risk for ACL injury.

~112~

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