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Analyzing sprint features with 2D Human Pose
Estimation
Koen van der Meijden
Anr. u504531 – Snr. 2017494
Master of science in Communication and Information Sciences
Master track Cognitive Science and Artificial Intelligence
Faculty of Humanities and Digital Sciences
Tilburg University
Thesis Committee:
Prof. dr. E.O. Postma
Dr. G.A. Chrupala
Date: 21-01-2019
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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Abstract
Current data science methods are able to estimate human pose from suitably recorded video.
For the analysis of video-recorded running behavior, this offers interesting opportunities for
computer-supported coaching of runners. The research described in this thesis focuses on the
computational analysis of video sequences of sprinting behavior. Former research in the
domain of sports sciences identified the following four main behavioral features to be
associated with good sprinting behavior: (1) leaning forward during acceleration, (2)
bringing the upper body upright after acceleration, (3) minimize vertical movement during
running, and (4) bringing the thigh up almost horizontally while running. We use a state-of-
the-art pose estimation method to extract these behavioral features from video sequences to
assess if they provide cues to assess or predict sprinting performance. The research question
addressed in this study is: To what extent do the extracted features correlate with sprinting
performance? The dataset used for this research consists of fifty videos of young athletes
running a trajectory of four times 16.5 meters and included the associated running
performances (i.e., time needed to complete the trajectory). Each frame of each sequence was
used as input for the pose-estimation method to extract the coordinates of 18 body part. The
extracted coordinates were transformed to obtain representations of the four behavioral
features. The representations were submitted to regression analyses to assess their correlation
with the overall sprinting performance. The results revealed that one of the four behavioral
features, i.e., (2) bringing the upper body upright after acceleration, indeed correlated with
running performance. For the other three behavioral features, no significant correlation could
be established. On the basis of these results, it can be concluded that bringing the upper body
upright correlates with sprinting performance and may be used for video-based assessment
and prediction of sprinting performance. For the other three features, additional studies are
needed to determine whether they either lack predictive value, or are not measured or
represented adequately.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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Table of contents
1. Introduction ................................................................................................................... 3
2. Related work .................................................................................................................. 6
2.1 OpenPose ....................................................................................................................... 6
2.2 Behavioral features of sprinting ...................................................................................... 7
2.3 Methods to analyze running behavior ............................................................................. 9
3. Methods ........................................................................................................................ 11
3.1 Dataset ......................................................................................................................... 11
3.2 Data preprocessing ....................................................................................................... 11
Horizontal correction ......................................................................................... 12
Change coordinate system ................................................................................. 12
Replace missing values...................................................................................... 12
Quality check .................................................................................................... 12
3.3 Data exploration ........................................................................................................... 13
Understanding the run ....................................................................................... 13
Second track ...................................................................................................... 14
Determining velocity ......................................................................................... 15
3.4 Experiments ................................................................................................................. 16
Experiment 1: Leaning forward during the acceleration phase ........................... 16
Experiment 2: Bringing the upper body upright during the constant speed phase 17
Experiment 3: Minimize vertical movement of the upper body during the constant
speed phase ....................................................................................................... 18
Experiment 4: Bringing the thigh up almost horizontally while running ............. 19
4. Results .......................................................................................................................... 21
4.1 Results experiment 1: Leaning forward during the acceleration phase ........................... 21
Conclusion ........................................................................................................ 21
4.2 Results experiment 2: Bringing the upper body upright during the constant speed
phase ................................................................................................................. 22
Conclusion ........................................................................................................ 23
4.3 Results experiment 3: Minimize vertical movement of the upper body during the constant
speed phase ....................................................................................................... 23
Conclusion ........................................................................................................ 24
4.4 Results experiment 4: Bringing the thigh up almost horizontally while running ............ 25
Conclusion ........................................................................................................ 25
5. Discussion ..................................................................................................................... 26
6. Conclusion .................................................................................................................... 28
Acknowledgements .............................................................................................................. 31
References ............................................................................................................................ 32
Appendix .............................................................................................................................. 36
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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1. Introduction
Between 1970 and 1980, running became much more popular all around the world. In
2013 almost 1.6 million people succeeded in finishing a marathon, where only 7% of these
runners are competitive runners with access to expert coaching (Scheerder et al., 2015,
Novacheck, 1998). Besides running as a sport on its own, there are other sports characterized
by running, such as American Football, basketball, baseball, soccer, field hockey. (Osgnach
et al., 2010). For many people, expert coaching on their running behavior is not accessible,
since expert coaching can be expensive and time-consuming (Luttik et al., 2018). By
improving running technique, not only performance increases, but also the risks of sport
injuries decrease (van Mechelen, 1992), leading to the possibility to increase the training
intensity or increase a person’s life-long health.
The methods that currently are being used for analyzing running behavior are
traditional methods with accelerometric sensors (Auvinet et al. 2002) or in a laboratory
setting (Wixted, et al., 2010). A modern computer-supported way to analyze videos is with
human pose estimation methods, which is less expensive than a laboratory setting and can be
used in the field. Examples of these methods are OpenPose (Cao et al., 2016) and DensePose
(Güler et al., 2018). The full potential of these methods is yet to be discovered. Some
research has already been done with human pose methods (Yao & Fei-Fei, 2010; Wang et al.,
2013; Yamaguchi, et. al, 2012), but none were applied to the domain of biomechanics in
sport. Former scientific research about the biomechanics of sprinting is described extensively
in section 2: ‘Related Work’. However, only one of the studies found attempted to study the
combination of running biomechanics and 2D Human Pose Estimation (Luttik et al., 2018).
The subject of this thesis elaborates on this specific combination of human pose estimation
methods and running biomechanics and is therefore a relevant scientific research subject.
This thesis research will search for possibilities in analyzing running video sequences by
using the human pose method, OpenPose. The dataset used for this research exists of fifty
videos of young football players running four times back and forth. The goal is to find out to
what extent it is possible to analyze these video sequences with the output of OpenPose and
whether we can find correlations between specific sprint features and performance (i.e., the
velocity of the athlete in the video sequence). The following four sprint features chosen to
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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analyze in this research are, according to the literature, the most characteristic features for
good sprinting behavior:
1) leaning forward during acceleration
2) bringing the upper body upright after acceleration
3) minimize vertical movement of the upper body during sprinting
4) bringing the thigh up almost horizontally while running.
The aim of improving sprinting performance, in combination with these four features leads to
following research question that will be addressed in this thesis: To what extent do the
extracted features correlate with sprinting performance?
Before answering the research question, this thesis will first deal with related work and
literature in section 2, where the four features will be discussed extensively. Also in this
section, OpenPose and the background of the research question will be discussed in detail.
The literature review has led to the following hypotheses regarding the influence of the
extracted behavioral features on running performance:
1. Leaning forward during the acceleration phase: The angle between the athlete’s neck
and hip should correlate with the performance. Were a larger angle leads to a higher
increase in the velocity.
2. Bringing the upper body upright during the constant speed phase: Running more
upright during the middle of a track should lead to a higher velocity.
3. Minimize vertical movement of the upper body during the constant speed phase:
Minimization of the vertical movement of the upper body should lead to higher
velocities of the runners in the videos.
4. Bringing the thigh up almost horizontally while running: A horizontal thigh of the
swing leg while running should result in higher velocities according to the theory.
Section 3 describes the method that was used for this research, including used models,
algorithms, the dataset, processing methods and the evaluation criteria. The fourth section
provides the results of this thesis. These results will be discussed in section 5 and section 6
represents the conclusion i.e., the answer of the research question of this thesis. Future
research opportunities will also be discussed in section 6.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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As mentioned, OpenPose will be applied to extract the coordinates of 18 body parts in
of each frame in every video in the dataset. From these extracted coordinates, the four
behavioral features can be analyzed, by e.g. calculating the angles between the hip and neck
to find out if the person in the video runs straight up or leans forward. The exact method will
be further explained in section 3. For each behavioral feature a regression model was made to
find the correlation between the sprint feature and performance.
The main finding of this study was that one of the four sprint features, i.e., 2) bringing
the upper body upright after acceleration, indeed correlated with running performance (p =
.027, R2 = .32). For the other three features, the models did not find a significant correlation
between the feature and performance. The results of this research can be a first step towards
automatically analyzing the running technique of runners and could become relevant for the
prevention of sports injuries and improving performance.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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2. Related work
This section explains in detail how OpenPose works and which former research has already
been done using similar 2D human pose estimation methods. Besides OpenPose, an important
part of this thesis deals with biomechanics of sprinting. Thus, the second part of this section
shows how the four behavioral features of sprinting were selected for the research question.
In the final part of this section, the emphasis is on examples from studies that used other
methods to analyze running technique.
2.1 OpenPose
Human pose estimation has been studied for well over 15 years (Sigal, L. 2014). According
to Singh (2016) human pose estimation has been applied for a variety of purposes.
Yamaguchi et al. (2012) used human pose estimation for cloth parsing, whereas Wang et al.
(2013) used a similar algorithm for pose-based action recognition. Yao & Fei-Fei (2010) did
research in human-object interaction with object and human pose methods. Yamaguchi et al.
(2012) and Yao and Fei-Fei (2010) used human pose estimation to extract poses from images,
whereas Wang et al. (2013) did use human pose estimation on videos, similarly to current
study. The study of Wang et al. (2013) tried to predict the action performed in the video by
using a human pose estimation algorithm. A similar algorithm is also implemented in the
OpenPose algorithm to estimate eighteen different body parts in a still image. OpenPose is
different from other similar algorithms because of its efficiency and the ability to detect
multiple persons in a 2D space (Cao et al., 2016). Figure 1 illustrates the global pipeline of
the OpenPose algorithm. To generate eighteen different body part coordinates in a still image,
OpenPose uses a neural network to simultaneously predict a set of two-dimensional
confidence maps of body part locations (figure 1b) and a set of two-dimensional vector fields
of part affinities. Next, the vector fields encode the degree of association between parts
(figure 1c). Finally, the confidence maps and the affinity fields are parsed by greedy
inference (figure 1d) (Cao et al., 2016). The parsing results create a stick figure of the human
pose in the still image, by connecting all the 18 body parts together (figure 1e).
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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Figure 1: Overall pipeline of the OpenPose algorithm. Figure A shows the input image. A convolutional layer network
creates confidence maps of body parts (b) and part affinity fields are shown in c. The parsing step (d) results in image e
(Cao et al., 2016).
For parsing a video, OpenPose repeats these steps for each frame in a video. The actual
output of OpenPose will not be the stick figure, shown in figure 1e. The output that can be
used to do further analysis is a table of all the coordinates of the eighteen body parts extracted
from the still image or every frame of a video.
2.2 Behavioral features of sprinting
According to Novacheck (1998) and Bosch & Klomp (2017) running can be categorized into
two different styles, namely running on a low constant pace and running on a high pace, like
sprinting. The dataset used for this thesis consists of side view videos of young football
players running four times 16,5 meters in a straight line, which be considered as sprinting.
The biomechanics of sprint running have been researched extensively in the past.
A traditional sprint can be divided into four different phases: (1) the start (block)
phase, (2) acceleration phase, (3) constant speed phase and finally (4) the deceleration phase
(Mero et al., 1992). (1) The start (block) phase is the phase where the athlete is in start
position and pushing off for his first stride. After the first stride (2) the acceleration phase
starts, toward the point that a maximum velocity has been reached. The time that an athlete is
running on his maximum velocity, is called the (3) constant speed phase. (4) The
deceleration phase is the phase where a sprinter will get tired, causing a decrease in the
velocity (Mero et al., 1992; Bosch & Klomp, 2017). Since the young football players in the
videos started from a standing position start, instead of a start block this phase could not be
analyzed. The deceleration phase was not usable either for analysis, because the athletes in
the videos were asked to slow down and turn around as fast as possible to run the 16.5 meters
back.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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As only the acceleration phase and constant speed phase could be analyzed from the
videos used for this study, the two other phases will not be elaborated on. The length of the
acceleration phase varies from 30 to 50 meters during a 100m race by sprinters on the highest
level (Mero et al., 1992, Jones, 2012). Meanwhile untrained sprinters tend to achieve their
maximal sprinting speed much sooner (Ae et. al., 1992; Delecluse et al., 1995; Majumdar, &
Robergs, 2011). About 75% of the total acceleration will be achieved in the first seven steps
(Jones, 2012; Moir et al., 2018). During these strides the ideal body alignment will change
from an angle of 45° toward about 65°, which equals to an increase of 3° per stride (Jones,
2012). During the first two steps of the acceleration phase the center of mass (COM) is just a
bit ahead of the contact point with the ground. At the fourth stride the center of mass is
already behind the contact point and is on the same horizontal position as the hip (Mero et al.,
1992; Jones, 2012; Bosch & Klomp, 2017). Figure 2 shows the optimal body alignment
according to Jones (2012). It explains how the COM is above the knee and contact point at
stride four and the two different body alignments of the first and seventh stride of the
acceleration phase.
Figure 2: Example of the different body alignments of the acceleration phase. The first stick figure shows the body alignment
of the first stride, the second figure of the fourth stride and the last figure shows the body alignment of the seventh stride of the acceleration phase.
Previous research using OpenPose was able to prove that leaning forward during a
sprint increases the velocity of the athlete (Luttik et al., 2018). Luttik et al. found that a
smaller angle between the hip and neck resulted in higher velocities. The finding of Luttik et
al. (2018), leads to the first essential sprint feature, i.e., (1) Leaning forward during the
acceleration phase. At the end of the acceleration phase, when the highest velocity has been
reached, the constant speed phase starts.
During the constant speed phase the body ideal alignment is almost 90° and the stride
frequency and length are increased to a maximum, which will result in short contact time
with the ground and higher running speeds (Billing et al., 2006). The body alignment during
the constant speed phase is the second feature to be tested in this study, i.e. (2) bringing the
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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upper body upright after acceleration. Other important features of this phase are the (hip)
stability (Young, 2006), arm swing and leg swing (Bosch & Klomp, 2017). Rotation in the
hip and shoulders should be as small as possible during a sprint. The arm swing is well
synchronized with the leg movement and only moves in the sagittal plane, thus ideally all
movements of the limbs are only forward and backwards (Bosch & Klomp, 2017; Young,
2006). The moment when the foot gets loose from the ground the thigh of the swing leg is
lifted far ahead of the body towards almost horizontal (Bosch & Klomp, 2017). If the leg
movement is correctly executed there should not be any vertical movement in the upper body,
which results in the third feature of the research question, i.e. (3) minimize vertical movement
of the upper body during the constant speed phase. When an athlete uses his energy for
vertical movement, his speed will automatically decrease (Bosch & Klomp, 2017). The
behavioral features of the constant speed phase are all meant for stability preservation, to
ensure that the body is able to move with maximal efficiency. Ideally, a sprinter’s head, neck
and spine should be neutrally aligned, which facilitates the optimal movement of the limbs
(Young, 2006). The optimal movement of the limbs can be described as front side mechanics.
An example of a front side mechanic is the movement of the thigh, which ideally reach a
horizontal line. (4) bringing the thigh up almost horizontally while running is the fourth and
last behavioral feature of sprinting that will be analyzed in this study. Better sprinters tend to
exhibit these front side mechanics to a greater extent and minimize the backside mechanics
(Mann, 1986; 2005; Mann & Hermann, 1985).
2.3 Methods to analyze running behavior
The majority of the biomechanics described in section 2.2 were found by applying methods
occurring primarily in laboratory settings. Because of the need for equipment for these
studies they are not easily transferable to the field (Wixted, et al., 2010). Accelerometric
sensors can be used for field research. With the use of accelerometric sensor athletes can run
in a more natural environment compared to a laboratorial setting. Auvinet et al. (2002) used
an accelerometric device to compare the kinematics and kinetics of seven elite middle-
distance runners under field conditions. The authors found that characteristic patterns in each
accelerometric axis could be used to identify initial contact, mid-stance and toe-off points
along with contralateral foot contacts. Accelerometric sensors are mostly used to study the
center of mass and kinetic biomechanics of running as stride frequency and stride length
(Wixted, et al., 2010; Billing et al., 2004). Billing et al. (2006) found a method to determine
the ground reaction force (GRF) from wearable instrumentation in middle distance running.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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They stated that not stride frequency, but stride length trough greater ground reaction force
predominantly leads to an increase in faster maximal running speeds (Weyand et al., 2000).
Billing et al. (2006) used neural networks and multiple regression models for his study about
GRF. Regression and correlation models were used by Morin et al. (2012) for a similar study
about GRF.
The use of accelerometric sensor and laboratorial settings to study running behavior is
expensive and time consuming. Because of the costs it can only be used by athletes on the
highest level or for research purposes. A simpler and a more accessible way to analyze an
athlete’s running behavior is through the use of mobile applications like Coach’s Eye, a low
cost application that can be used by athletes and coaches to analyze a (slow-motion) video by
pausing the video at every frame and finding patterns of running features in a still image.
Research about the use of Coach’s Eye and similar applications were not found, but
applications like Coach’s Eye are useful for expert analysis in sports and available for the
public. The Coach’s Eye application has been downloaded over 1 million times and is used
by several expert coaches in different sports, e.g. Jeremy Fischer who is a coach of several
Olympic track and field athletes.
In the domain of sports sciences, video analyses alone are not often being used for
studying biomechanics. More frequently they are used in combination with other methods,
like accelerometric sensor and treadmills, e.g. Belli et al. (2002) used 2D video analyses in
combination with 3D ground reaction force measurements to calculate the joint moment and
power of the lower limb in running. Seifert et al. (2004) used video-only analysis for their
research, but it was implemented on the biomechanics of swimming. Stroke rate, velocity and
index of coordination were calculated from these videos. The study of Seifert et al. (2004)
used correlation and regression methods to find relationships between the behavioral features
of swimming. Regression and correlation methods are also the evaluation methods used in
current thesis, similar to the methods of Seifert et al. (2004) and Morin et al. (2012).
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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3. Methods
This section describes the methods that were used in this thesis to analyze the dataset.
Paragraph 3.1 explains how the videos have been transformed into actual data. The next two
sections describe the preprocessing methods and exploration of the data. Finally, section 3.4
goes into detail about the experimental methods that have been used to answer the research
question.
3.1 Dataset
The set of data that has been used for this research consists of 50 videos. The videos were
made available by professor Wim Nuijten, who explores the use of automatic pose-estimation
methods for the development of a coaching app for young athletes (Luttik et al, 2018). In
these videos’ athletes between the age of 10 to 12 years old run two times 16.5 meters back
and forth between cones, so four tracks in total. The algorithm of OpenPose extracts every
human pose in the video into data points, regardless of the quality of the video. Thus, a
simple smartphone camera has been used to shoot the videos on a full-HD (1080x1920
pixels) resolution with 60 frames per second. The footage was shot with a tripod to minimize
possible noise caused by unstable recordings. Depending on the athletes’ speed, the video-
lengths varies from 16 to 22 seconds long.
OpenPose transformed these videos into actual data by extracting coordinates from
eighteen different body parts per frame. The operation of OpenPose has already been
discussed in section 2.1 and further details can be found in the paper of Cao et al. (2016). The
output of the OpenPose algorithm is a CSV-file consisting of six columns. The first column is
the file index, the second column consist of the frame index and the third column is the body
part index, which could have the value of 0 to 18. The fourth and fifth column are the x and
y-values of the body parts, these two values combined result in the location in pixels of the
body part in a specific frame. The last column gives the confidence score of the body part x-y
combinations, which is the confidence the network has that a certain pixel contains a certain
body part. In total this extraction resulted in a dataset of about 1.080.000 data points to
analyze.
3.2 Data preprocessing
The following preprocessing methods described were developed by Menno van Leeuwen. A
student from the Jheronimus Academy of Data Science in ‘s-Hertogenbosch. Van Leeuwen
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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has conducted a similar study that used the same dataset (Leeuwen, 2019). Three different
preprocessing methods have been used to clean the data.
Horizontal correction. Despite the fact that the videos were recorded with a tripod.
Van Leeuwen (2019) found out that not every video was recorded on water level, which
resulted in difficulties in comparing the different frames of each video and in comparting the
different videos with each other. To find out what the exact correction should be to bring the
video down to water level, the angle of the ankle between the starting point and the middle of
the video was calculated. At this point, the coordinates of the ankle at the starting point and in
the middle of the video are known. With the horizontal and vertical difference between these
two points, Pythagorean theorem was used to calculate the angle correction. The corrections
for all videos was calculated between an angle of minus .5° and 3°, the mean of all the
corrections is about 1.14°. The improved data is saved in an additional column in the data
frame. The corrected points were used for more accurate measurement in the experiments.
Change coordinate system. This correction is not really necessary, but it makes it
much easier to read the data. The output from OpenPose gave the vertical coordinate of 0
pixels as the highest point in the video and 1080 pixels as the lowest point of the video. Thus,
according to the OpenPose output, the coordinate of the bottom-left corner of the video is
1080, 0, instead of 0,0. So, this correction basically swaps the vertical coordinates around to
make it easier to read the data and make more sense of the results.
Replace missing values. The dataset consisted of numerus missing values, which
were replaced a linear interpolate function from the pandas python package. The interpolate
fills the missing values by calculating the mean of the two neighbor values.
Quality check. Before running the data through the experiments, the dataset was
cleaned through a quality check. This function checks whether the data in each video had too
many missing values to use it for the experiments. Since the total dataset only exists of fifty
videos, we need to be careful how much data we dropped. The baseline for this check was set
at the mean of missing values per video minus the standard deviation.
Quality check = �̅� − √∑(𝑋−�̅�)2
𝑛 (1)
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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This check resulted in a total drop of four videos from the fifty videos that this
research started with.
3.3 Data exploration
As described in section 2, a normal sprint can be divided into four different phases. The runs
preformed in the videos are not traditional sprints like often being studied. The runners in the
videos did not start from a start block and stopped abruptly to turn around. That is why the
start (block) phase and the deceleration phase were absent in the videos, and only the
acceleration phase and constant speed phase were used for this experiment. Van Leeuwen
(2018) found a way to split the complete run into four different tracks, after which these four
tracks could be split into the different phases mentioned in the literature review.
Understanding the run. Each football player was asked to run back and forth two
times between a set of cones (so each of them ran the distance four times). Every video was
divided into four different tracks by finding the turning points of the young football players.
Figure 3 illustrates the whole run in the first image, the subplots below illustrates every track
separated by the turning points. The turning points were found by peak determination. This
means that the middle peak in figure 3 is the point that the hip of the athlete has moved to the
right to the furthest horizontal point in the video, which is shown in picture 1 for a better
understanding. This is the moment that the runner starts his third part of the run.
Picture 1: A still image of one of the videos. The point that the athletes ran one time back and forth and has moved to the
right to the furthest horizontal position in the video.
The start can then be found by cutting the run off at the position of the central peak.
The same holds for finding the endpoint of the four tracks together. A similar method was
used to find the starting point for track two and four. In contrast with the first method, this
time the leftmost horizontal position of the hip was determined. Finding the point of the hip
on the left of the video resulted in two downward peaks, e.g. is visualized in figure 3.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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Figure 3: Visualization of one video of the dataset. First figure shows the structure of the whole video. The peaks and valleys
were used to calculate the cut-off points. Track 1, 2, 3 and 4 shows the structure of every run separately, with three clear
phases of sprinting.
In addition to the entire run, the bottom part of figure 3 shows the four tracks
separated from the complete video as well to visualize the structure of the four tracks. The
first curve of every track illustrates the acceleration phase, the straight part shows the
constant speed phase and the short curve in the end of every track can be seen as the
deceleration phase. The reason that the curve at the end of every track is short, is because it is
a forced deceleration to turn around.
Second track. By splitting the complete run into four different tracks the sprint
phases can be extracted, but exploratory data analysis did show unstable results in specific
tracks. The runners showed different behavior in the different tracks, resulting in an uneven
distribution of the running speed in each of the tracks. Table 1 shows the uneven distribution
by the differences between the mean and standard deviation of the duration of every track.
Figure 4 illustrates the distribution of the duration in frames of every track. Therefore, only
track number two is used for performing the actual experiment of section 3.4.
Table 1: Mean and Standard deviation of the duration (in frames) of every track.
Track 1 Track 2 Track 3 Track 4
Mean 231,3 223,3 234,1 221
Standard deviation 59,6 12,7 14,2 32,5
Figure 3 showed an almost straight line at the end of track four. While running track
number four every athlete had the opportunity to run across the finish line, without
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
15
decelerating, while other athletes chose to stop at the end of track four, similarly to the other
tracks were every athlete needed to stop to turn around. This causes the non-normal
distribution of the duration of track 4 in figure 4. Track 2 shows overall the cleanest
distribution of the duration of the run, with a standard deviation of only 12,7 and a clear view
on the acceleration phase and constant speed phase. Track 3 could have been used to check
the results of track 2. But because of time limitation only track 2 has been used for the
experiments.
Figure 4: Histograms of the distribution of the duration (in frames) of every track. Track 2 and 3 overall have the most
similar normal distribution. Athletes are about 10 frames slower in track 3, compared to track 2. Track 1 and 4 are
differently distributed because of the start and the finish.
Determining velocity. The velocity has been chosen to be the best measurement for
the performance of the athletes, which can be used as the dependent variable in the regression
models. The simplest way to calculate the average velocity is to determine the actual duration
that the athlete needed to execute the complete run. Considering that we only analyze the
second track in our experiments and used only a specific phase in the second track, another
method for determining the velocity has been used.
The average velocity that an athlete ran over a specific part in the video can simple be
calculated by the equation:
𝜈 =𝛥𝑥
𝛥𝑡 (2)
In this equation Δx stands for total distance that the athlete ran and Δt for the time it took to
complete the task. The Δx cannot be measured in meters, but only in pixels. This
measurement in pixels can be determined by the distance traveled of the hip over the x-axis
of the video. Next, we divide this distance with the time (Δt) in frames, which results in a
solution, velocity(𝜈) in pixels per frame. This method can be used in every part of the video,
thus it will be used in every experiment for this thesis. The velocity of a particular part in the
video is called the local velocity.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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3.4 Experiments
Experiment 1: Leaning forward during the acceleration phase. The first
experiment tested the theory that leaning forward during the acceleration phase increases an
athlete’s velocity (Bosch & Klomp, 2017; Luttik et al., 2018). The expectation is that leaning
forward more during the early stages of a track results in a higher local velocity. To prove
this hypothesis, two variables are necessary to extract from the data, the local velocity of the
acceleration phase and the maximum angle between the hip and the neck during this phase. A
smaller angle indicated that the runner was leaning forward to a greater extent. For this
experiment the maximum angle has been used, since it means that the runner did not run
more upright during this phase than the outcome of the maximum angle. Thus, the runner
with the lowest maximum angle was leaning forward the most and a negative correlation is
expected between the angle and velocity. To calculate the local velocity of the athlete in this
phase, equation (2) was used to calculate the velocity of the specific phase. Figure 5
visualizes three examples of stick figures in a running pose, for a better understanding of the
angles between the hip and neck.
Figure 5: Examples of different leaning angles while running. First image is an example of one of the outliers in figure 10 with an angle of 10°. The second image illustrates the mean angle of all athletes during the acceleration phase. Finally, the
image visualizes the mean angle of all athletes during the constant speed phase.
For the exploration phase of experiment 1 we created fifty plots of every athlete with
the angles between the hip and neck of the first 70 frames. The figures showed some
interesting information. Two of these figures are illustrated in figure 6. In the majority of the
figures a sudden decrease in the angle between neck and hip was found around the 20th frame
mark. After that sudden drop the angle increases slowly toward a maximum value. In most of
the videos, this maximum value lays around the 50° to 60°. The reason for this drop could be
that the athlete tries to put as much power in his first stride as possible by leaning forward.
By leaning more forward, the center of mass moves forward and a faster acceleration can be
achieved.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
17
Figure 6: (Left) shows a typical example of an athlete’s acceleration phase. During his first stride he leans forward a lot to attain more power, afterwards he slowly comes up, towards an angle of 55°. (Right) around the 20 frames the athlete leans
forward and comes up, but every stride after his first is similar to its previous stride.
Figure 6 shows two different examples of the acceleration phase. The left graph
shows an example that should be optimal, according to theory. The athlete starts low after his
first stride. Every stride after that, his angle of body alignment increases towards 55°. The
second example has a similar starting position, but already with a bigger angle, of 32°, after
his first step. Afterwards, the angle stays the same during the next steps, at about 53°. To test
if these angles have an impact on the velocity of an athlete the Pearson correlation test will be
used for this experiment, as well as an scatter plot with an regression line to illustrate the
correlation.
Experiment 2: Bringing the upper body upright during the constant speed phase.
The second experiment is very similar to the first experiment, but for this experiment the
constant speed phase will be analyzed. According to previous research results, the body
alignment should be straight instead of leaning forward during the constant speed phase
(Young, 2006). Thus, the difference between the first and second experiment is the frames of
the video that are analyzed. Another difference is that the regression model needs the
minimum angle between the hip and the neck as an independent variable.
The constant speed phase was chosen between the frames 100 to 175 of the second
track, since these frames indicate the middle of the track. Figure 3 shows that between those
frames the velocity does not increase anymore and the speed is constant. The velocity
between these frames is used as the dependent variable for the Pearson correlation test.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
18
Figure 7 illustrate two different examples from the data. The graph on the left shows
an example of an athlete who is definitely in his constant speed phase, while the graph on the
right shows an athlete who could still be accelerating by cause of the increase in the angle
between his neck and hip from frame to frame.
Figure 7: (Left) illustrates an example of an athlete who is actually in his constant speed phase. The variance of the angle is around 20°. (Right) the graph is an example of someone who is according to his leaning angle still accelerating. Every
stride the angle gets bigger.
Experiment 3: Minimize vertical movement of the upper body during the
constant speed phase. Previous research mentioned that vertical movement of the upper
body could decrease the velocity and that the hip stability is an important feature for running
(Bosch & Klomp, 2017). In perfection, during the constant speed phase every move of the
human body is made to move horizontally (Mann, 1986). This experiment was performed to
find a correlation between minimizing vertical movement and running speed and to determine
whether the hip stability could be a predictive variable for running velocity.
The local velocity for experiment 3 is the dependent variable of the regression model.
Whereas the hip bounciness (or vertical hip movement) is the independent variable.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
19
Figure 8: Figure 8 illustrates the hip bounciness of two different athletes during track 2 between frames 100 and 175. Athlete 1 has a significantly lower variance, compared to athlete 2. The right graph shows a variance of almost 3 times
bigger than the left graph.
The data presents promising differences between the athletes as shown in figure 8.
The athlete in the left graph of figure 8 has a vertical hip variance of about five pixels,
whereas the right graph shows a variance of about fifteen pixels. The hip bounciness in this
experiment is shaped by taking the difference of the minimum and the maximum of the hip
on the y-axis of every athletes’ vertical hip movement between frame 100 and 175. The local
velocity is calculated between those framed and used as dependent variable in the correlation.
Experiment 4: Bringing the thigh up almost horizontally while running. The last
experiment revolves around the angle between the knee and hip during the constant speed
phase. This angle was used to find the height of the knees during track 2, which indicates the
horizontalness of the thigh. The hypothesis is that the height of the knee has a positive
correlation with running velocity. The angle between the knee and the hip is the independent
variable in this experiment, the local velocity has been used as the dependent variable.
Figure 9: Figure 9 illustrates the angle between the knee and hip of two different athletes during track 2 between frames 100
and 175. The athlete of the left graph lifted his knee higher than the athlete of the right graph. (Left) shows a minimum angle
of about 20°, whereas the right graph had a minimum of almost 40°.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
20
Figure 9 illustrates two examples of different athletes and their thigh movement. The
left graph shows a more spread out variance compared to the right graph, which tells us that
the runner from the left graph lifted his thigh higher than the runner in the right graph.
Followed by the theory mentioned in the literature review, the expectation is that runner 1
(left graph) is faster than runner 2 (right graph) in this example. Experiment 4 tests the theory
that bringing the thigh up almost horizontally results in higher velocities by finding
correlations between the minimum angle of the runners between the frames 100 and 175 of
the video and the local velocity between these frames. An angle of 0° between the hip and
knee, means that the thigh is horizontal.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
21
4. Results
The result section presents the results of the experiments described in section 3.4:
experiments. Every paragraph consists of the experimental results and a small discussion of
the most important findings. The overall discussion and conclusion can be found in sections 5
and 6.
4.1 Results experiment 1: Leaning forward during the acceleration phase.
A simple linear regression model was used to visualize the correlation between the local
velocity and maximum leaning angle of every athlete during the acceleration phase. A small
negative correlation was found by the Pearson correlation test (p = .17, R2 = -.21), which is a
non-significant result according to statistical analysis. The slope of the regression line, shown
in figure 10, is -.013. The data points are spread out over the field, which means that the
standard error of the regression model is high, this was also supported by the results of the
square of Pearson’s rho.
Figure 10: A scatter graph between the maximum angle between the hip and neck during the acceleration phase and mean velocity in this phase. The figure illustrates a small negative correlation, but also a spread-out field of data points.
Conclusion. Previous studies observed consistent results about the sprinting feature
analyzed in this experiment (Jones, 2012; Bosch & Klomp, 2017). Luttik et. al (2018) found a
correlation between the duration of the run and leaning forward with the same method and
dataset. The results of current study indicate that leaning more forward during the
acceleration phase increases the velocity, but not with a significant amount.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
22
4.2 Results experiment 2: Bringing the upper body upright during the constant speed
phase.
The statistical results of experiment 2 were similar to experiment 1. The results of the square
of Pearson’s rho between the minimum angle of leaning forward and the velocity in de
dataset was R2 = 0.09, figure 11.2 shows the regression line of this correlation. Because of the
unexpected results a second test was performed with the mean angle between the hip and
neck as independent variable instead of the minimum angle, which resulted in R2 = 0.17 (p =
0.27) A minor correlation was perceived by the square of Pearson’s rho between and the
mean velocity during the constant speed phase of track 2, R2 = 0.17 (p = 0.27), the regression
model of the second test is shown in figure 11.1. Both figures, but especially figure 10.2, is
shown that multiple outliers influence the regression model.
Figure 11: (Left) illustrates a scatter plot between the mean velocity during the constant speed phase and the mean angle
between the hip and neck during this phase of the athletes. (Right) shows the same scatter plot, the only difference is that it takes the minimum angle between the hip and neck instead of the mean angle. The mean angle shows a larger correlation.
It is not reasonable to expect that an athlete runs with an angle between his hip and
neck of less than 10° as some of the outliers in the right graph of figure 11 indicate. Figure 5
shows an example of a stick figure in a running pose, with an angle of 10° between the hip
and neck, which is almost horizontal. With the expectation that the outliers in figure 11 were
caused by errors due to OpenPose and the quality of the video, a new regression model was
built without the outliers. The outliers were determined by excluding all values varying more
than two times the standard deviation from the mean, which mean that every athlete with a
minimum of less than 13.1° was excluded.
The results of the model without outliers that used the mean leaning angle between
hip and neck as the independent variable were R2 = .32, p = .027, production statistically
significant results. The results of the model using the minimum angle as predictive variable
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
23
were also better, R2 = .24, p = .13, but these results need to be interpreted with caution,
because of the non-significant p-value. The regression models of the third and fourth tests are
visualized in figure 12 in a scatter plot.
Figure 12: This figure illustrates two scatterplots with the same variables as in figure 11, but without extreme outliers.
Conclusion. One of the four regression models performed in this experiment resulted
in significant results. All models showed that running more upright had a positive effect on
the velocity, which is supported by the expectations mentioned in the introduction. The mean
angle between hip and neck as a predictive variable with filtering outliers was the only model
resulting in significant results and had the highest correlation. The results corroborate the
findings of a great deal of the previous work in the study of Bosch and Klomp (2017) and
Young (2006). Young (2016) stated that a more upright upper body increases the stability
while running, which tends to increase the maximum velocity.
4.3 Results experiment 3: Minimize vertical movement of the upper body during the
constant speed phase.
The third experiment aimed to find a negative correlation between the vertical hip movement
during the constant speed phase and the velocity. This would mean that less bounciness in the
hip results in higher velocities of the athletes. The first regression model of this experiment
found a positive correlation (p = .076, R2 = .26). The model used the absolute vertical hip
movement as independent variable. Another model, with the standard deviation of the vertical
hip movement as independent variable, achieved similar results (p = .054, R2 = .29).
Surprisingly, both squares of Pearson’s rho concluded a positive correlation, which is
contradictory with the expectations, but given the low p-value we assume this is due to noise.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
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Figure 13: (Left) illustrates the correlation between the velocity and vertical hip movement during the constant speed phase.
(Right) shows the same correlation with the standard deviation of the vertical hip movement. Both show similar results.
The contradiction between these results and the expectations based on the theory can
easily be explained by the outliers, as shown in figure 13. It is not likely that an athlete had an
absolute hip bounciness of more than 70 pixels, therefore two more tests were performed for
experiment 3. The third model only included athletes with a vertical hip movement lower
than 49.4 pixels, furthermore it was similar to the first test. The same holds for the fourth test
where the outliers above a standard deviation of 14.7 were removed. The outliers were
determined by excluding all values varying more than two times the standard deviation from
the mean. The third model resulted in a correlation of R2 = .036, p = .82 and the fourth in R2 =
-.027, p = .86. The results of these correlational analyses are presented with a regression line
in figure 14.
Figure 14: (Left) shows a scatter plot of the correlation between the absolute vertical hip movement and mean velocity with
consideration of outliers. (Right) shows the same figure but uses the standard deviation of the vertical hip movement as
independent variable.
Conclusion. The first two models of experiment 3 showed positive results between
the vertical hip movement and the mean velocity of the athletes during the constant speed
phase. This means that more hip movement would results in higher velocities. This finding is
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
25
contrary to previous studies which have suggested that less hip movement result in higher
velocities (Bosch & Klomp, 2017; Young, 2006). Furthermore, the two models that were
used later on, were not able to find any significant result or correlation. The models tested on
the data without outliers suggested that vertical hip movement does not have any impact on
the velocity.
4.4 Results experiment 4: Bringing the thigh up almost horizontally while running.
The last experiment calculates the correlation between the local velocity of the constant speed
phase and the minimum angle between the knee and hip. The test showed a small correlation
(p = .16, R2 = -.21). The non-significant p-value of 0.16 shows that the results need to be
interpreted with caution.
Figure 15: Correlation scatterplot of the mean velocity and the minimum angle between the hip and knee of every athlete
during the constant speed phase. The data points are spread out, but still illustrates a small negative correlation.
Conclusion. The minor correlation found in experiment 4 means that the higher the
athlete lifts his thigh during the constant speed phase, the faster the athlete runs. The result
corresponds with the theory discussed in section 2. The downside of this result is that the
correlation was found to be not significant, like it was the same in experiment 1 and 3. The
data points, shown in figure 15, are not in line with the regression line, and have a spread-out
structure. The spread of the data points visualizes a high standard error.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
26
5. Discussion
The aim of this thesis was to find out whether human pose estimation would be able to
analyze sprinting behavior through videos by using OpenPose. To evaluate the results linear
regression models and the square of Pearson’s rho were used. During the first stage of the
thesis, the most characteristic features of sprinting technique to analyze were extracted from
the literature. Previous research in the domain of sports sciences identified the following four
main behavioral features to be associated with good sprinting behavior: (1) leaning forward
during acceleration, (2) bringing the upper body upright after acceleration, (3) minimize
vertical movement during running, and (4) bringing the thigh up almost horizontally while
running. These four features were divided into four different experiments.
With the second experiment we found evidence for the fact that bringing the upper
body upright during the constant speed phase causes higher maximum velocities. The reason
for this fact was explained in a study by Young (2006). An upright posture with a posteriorly
rotated pelvis ensures freedom of movement and facilitates relaxation while running, both of
which enhance elastic energy return from the core and extremity musculature. It also
advances the athletes front side mechanics and limits backside mechanics (Young, 2006).
Front side mechanics refers directly to the second interesting finding of this study, which was
the correlation between bringing the thigh up almost horizontally while running and the local
velocity of the constant speed phase. Although, the correlation found in experiment 4 was not
significant (p = .16, R2 = -.21), the minor correlation agrees with the findings of Mann (1986;
2005) and Mann and Hermann (1985), who found that better sprinters tend to exhibit greater
front side mechanics and minimize backside mechanics. A similar correlation coefficient was
found for experiment 1 (p = .17, R2 = -.21). This correlation corroborates with the findings of
Luttik et al. (2018) about leaning forward while running. The difference between the current
study and the study of Luttik et al. (2018) is that the current study only examined the sprint
feature during the acceleration phase, instead of the entire sprint. Despite the fact that the
minor correlations found in experiment 1 and 4 corroborate with former studies, the
correlations found were small and not significant.
The non-significant results of experiment 1, 3 and 4 could have been caused by the
noisy output of OpenPose. The output of OpenPose consists of many missing values and
outliers. For most part the data was cleaned by preprocessing methods described in section 3.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
27
Various values were replaced by the interpolate function, still after interpolating the dataset
missing values still occurred. A solution for this problem could be to extract the coordinates
from the videos by using DensePose instead of OpenPose. DensePose is a continuation of the
OpenPose algorithm. DensePose uses a region with convolutional layers network (R-CNN) to
visualize a 3D surface of coordinates of the complete human body in an image or video
(Güler et al., 2018). DensePose has proven to be more accurate than other pose estimation
methods (Güler et al., 2018).
The tested experiments were based on behavioral features of sprinting found by
previous research, e.g. from Bosch and Klomp (2017), as well as Young’s (2006) research.
These studies were performed on adult athletes and/or professional athletes. A possible
explanation for the non-significant results could be that the sprint features analyzed in this
thesis does not have much influence on the velocity of children running. The motor skills and
muscle strength of children can differ a lot (Schönau, 1996). A child with more muscle
strength and better motor skills could run faster with a bad running behavior, than a child
with good running behavior, but a less developed body.
Another possible explanation for the results found could be, that there was not enough
data available for significant results. If the model receives more input data, outliers will be
easier to classify and the underlying distribution of the data will be much clearer. To gather
more data for the human pose estimation algorithm, additional videos of athletes running the
same task would need to be shot. However, this would be time consuming and lead to more
videos that are unsuitable for this research. The task that the athletes had to do in current
recorded videos is a doubtful method to analyze running behavior, because of the unclear
instructions for the athletes and the rotation point in de videos makes it difficult to analyze
the behavior. To analyze specific phases of sprinting, preprocessing and data exploration
were required for using this dataset. It would be preferable for the analysis to record one
specific phase in each video. For example, to analyze the acceleration phase it would be
easier if only the first 10 to 20 meters would have been recorded. If further research goes
deeper into the constant speed phase, the videos should consist of athletes running only 15
meters already at maximum speed. In this way, the task would be clear-cut for the athletes to
perform and differences caused by external factors, e.g. the dissimilarities between the four
different tracks and unclear data from the rotation points, would be less likely to occur.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
28
In general, this thesis has shown that there are definitely opportunities to analyze
running behavior from videos, recorded with a simple camera setup, using human pose
estimation methods by finding significant correlations for one out of four behavioral features
of sprinting. However, to offer computer-supported coaching by analyzing video recorded
running behavior to the public, the subject needs further research.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
29
6. Conclusion
The current study was designed to determine to what extent the extracted features correlate
with sprinting performance by using 2D human pose estimation. The four different sprint
features that were selected to analyze their influence on sprinting performance, were:
1) leaning forward during acceleration
2) bringing the upper body upright after acceleration
3) minimize vertical movement of the upper body during sprinting
4) bringing the thigh up almost horizontally while running.
Based on theory, the expectation was that: 1) leaning more forward during acceleration
would lead to a better sprint performance. 2) After acceleration bringing the upper body
upright, should lead to more stability while running and therefor also a higher velocity, as
well as 3) Minimization of vertical movement of the upper body during sprinting. Feature 4,
4) bringing the thigh up almost horizontally while running ̧should advance better front side
mechanics while running and therefor also better performances.
The results revealed that one of the four behavioral features, i.e., (2) bringing the
upper body upright after acceleration, indeed correlated with running performance. For the
other three behavioral features, no significant correlation could be established. On the basis
of these results, it can be concluded that bringing the upper body upright correlates with
sprinting performance and may be used for video-based assessment and prediction of
sprinting performance. The other three features bring a fruitful area for further research.
A suggestion for further work is to repeat this study using DensePose instead of
OpenPose. The network of DensePose is trained on the same data set as the OpenPose
network but aims to map all human pixels of RGB image to the 3D surface of the human
body. DensePose tends to perceive less noise in the data, which could lead to better results.
Another possible solution for less noise could be, using more professional camera equipment,
like was done in a research by Seifert et al. (2004).
Secondly, the lack of enough data was an obstacle for this study. Further research
should be carried out to establish more data, by adding new videos to the dataset. When
gathering new data, another setup should be considered as, suggested in the discussion
section of this research, filming the separate running phases. Through this approach, it will be
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
30
less time-consuming to prepare the data and it also leads to more cleaner data. Gathering new
data with a specific setup, also gives an opportunity to research the other two sprint phases,
i.e. the start block phase and deceleration phase, which could be interesting for more
professional athletes. Further research opportunities are also found at the subject of running
on a low constant pace. Human pose estimation can also be used for analyzing videos of
athletes who run on a slower pace, which could expand the existing framework of 2D human
pose estimation and the domain of running biomechanics. This research has focused on the
domain of sprint biomechanics. Much more research is needed to determine the running
features, related to running-related injuries and the possibilities of prevention.
ANALYZING SPRINT FEATURES WITH 2D HUMAN POSE ESTIMATION
31
Acknowledgements
I acknowledge the effort from my thesis mentor, E. Postma, for his feedback during the
period of writing this study. Besides my mentor, I want to thank M. van Leeuwen for his
specific programming help and making part of his code available for this research. Finally, I
want to acknowledge W. Luijten for sharing the dataset consisting of fifty videos of running
athletes.
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32
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Appendix
All figures, plots, code and the dataset used and made for this thesis can be found in a
separate appendix folder, since the total amount of files used for this thesis was equal to over
300 files.