Estimation and Classification of

Human Movement Using 3 Axis Accelerometers

Eric CopeAdvisors:

Dr. Antonia Papandreou-Suppappola

Dr. Bahar Jalali-FarahaniMarch 30, 2009

2

Motivation

3

Qualifier's Summary Brief Background Human Physiology Sensor Technology - Accelerometers Formulation of Human Movement using

Accelerometer (gravity, movement, noise) Solutions for two models using Kalman Filtering Simulations Future Work

4

About Me: Eric Cope

EducationBSE – Electrical Engineering – ASU – 2004

Focus: Analog Circuits, DSP, RF

MSE – Electrical Engineering – ASU – 2006 Focus: Analog Circuits, DSP

PhD – Electrical Engineering – ASU Focus: DSP and VLSI Implementation

5

About Me: Eric Cope

ProfessionMedtronic

2003-2004 – Sensors Manufacturing Intern 2004-2005 – Product Development IC Design Intern 2005-2006 – IC Design Engineer (PD) 2006-2008 – Senior IC Design Engineer (PD) 2008 – Senior IC Design Engineer – Digital

Technologies

6

The Physiology of Human Movement States

Walking / RunningStanding / leaningSitting

Slouching, leaning forward)

Lying Down Propped Up Stomach, side, back

Transitory StatesStanding to SittingSitting to Lying

DownStanding to Lying

Down (falling)

7

Medical Implications of Human Movement

Quality of Life Measurement Disease Detection

Heart FailureFall Detection – AMI, Syncope

Activity Detection / Estimation Objective Measurement of Activity Obesity Impact

8

Sensor Technology and Their Benefits and Costs

Sensors

Metric Accelerometers Magnetometers Gyroscopes Cameras

Power Low Power Mid Power High Power Very High Power

Size Small Small Medium Very Large

Cost Cheap Less Cheap Less Cheap Expensive

Low to Mid Low to Mid Low to Mid Very High

Yes No No No

Effectiveness Yes No Yes Yes

Processing Requirements

Implantablity

9

Why Accelerometers in Implantable Medical Devices? Low Power - <200nA Cheap

MEMS technology enables mass productionCMOS technology allows calibration of low

reproducibility processing -> easy to manufacture

Low Processing NeedsPiggybacking other medical device needs

10

Types of Accelerations

Linear Acceleration w.r.t. to direction vector Ex: a runner accelerating in a straight line

Angular Acceleration w.r.t. to direction vector As an object rotates around a point, it is experiencing an

acceleration always pointing to the point about which it is rotating Ex: Planetary motion Theta is the time-varying angle of the circular direction

2

2

dt

xdta 2

21 )()()0()( ttattvxtx

2

2)()(

dt

tdta

11

Types of Acceleration

GravityPulls bodies towards one anotherAmplitude depends on the masses of the

bodiesEarth's gravitational pull is 9.81m/s2

Forward Thinking: How do we Differentiate between these types of accelerations?

12

These Accelerations as Experienced by the Human Body Linear

Gravity, standing to walking Angular

Bending over to pick up a pencilSpinning like a topDancing

13

But, What Else Does the Sensor Experience? Offset

Mechanical Changes Drift in Circuit Performance

Noise EMI – AWGN Narrowband (60 Hz) and broadband (RF radiation) Muscle Spasms – AWGN bandpass noise pulses Voices – broadband bandpass Cross-Axis Contamination - nonlinear (strong sensor characterization

needed) Circuit Noise – AWGN broadband - well modeled and understood

14

Frames of Reference

Global Frame of ReferenceGravity always points in -Z directionThe sensor is fixed with respect to the EarthEx: Needle of a compass

Physiological Frame of ReferenceThe sensor is always aligned with the PatientGravity can point anywhere

15

Current Published Research

Two Groups (1) Heavy Emphasis on Biologics, Light

Emphasis on DSP Lots of light post processing: low pass filtering with

lots of tweaking to obtain data per a particular sample set

Lots of Sensors: Magnetometers, gyroscopes, accelerometers, well powered externally

Large majority of the papers found lie in this category

16

Current Published Applications

Gesture Movement Detection – Wii Athletic Optimizations Adaptive Noise Canceling of ECG Signals Human Movement

Knee Unlock – FallingMonitoringHeart Movement – HFRate Response

17

Current Published Research (2) Heavy Emphasis on DSP, Light Emphasis on

Biologics Intense complex processing No direct application Ex: sensor fusion techniques not applicable to the field

Current Methods Simple Processing

Simple filtering Thresholding

Neural Networks Adaptive Filtering Kalman Filtering

18

Published Example of Kalman Filtering of 3-Axis Accelerometers P. Veltink et-al were processing a 3-Axis Accelerometer’s

data stream using Kalman filtering to establish an inclination measurement Inclination is the difference between the global frame of

reference and sensor (or patient) frame of reference ARMA Acceleration Modeling, Kalman Filtering of Estimation

Errors, Autocalibration of Offset Error Estimation Their application was an external application, however, it

had potential to work in an implantable mode

H. J. Luinge and P. H. Veltink, \Inclination measurement of human movement using a 3-D accelerometer with autocalibration," IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp. 112{121, 2004.

19

Overview of Kalman Filtering: Predict The optimal solution is when state space equations are linear and noise and

modeling errors are Gaussian Prediction:

Predicted Estimate Covariance:

covariance noise process -

covariance updated -

covariance predicted -

1

1|1

1|

11|11|

k

kk

kk

kTkkkkkk

Q

P

P

QFPFP

error modeling -w

valueupdated - ˆ

modeln transitiostate -

data 1-k using valuepredicted - ˆ

ˆˆ

k

1|1

1|

1|11|

kk

k

kk

kkkkkk

x

F

x

wxFx

noisen observatio -v

modeln observatio -

nobservatio -

ˆ

k

k

k

kkkk

H

z

vxHz

20

Residual (or Innovation):

Innovation Covariance:

Optimal Kalman Gain

Overview of Kalman Filtering: Update Updated State Estimate

Updated State Covariance

GainKalman Optimal -

11|

k

kTkkkk

K

SHPK

covariance noisen observatio -

estimate covariance predicted -

covariance innovation -

1|

1|

k

kk

k

kTkkkkk

R

P

S

RHPHS

matrixindentity -

covariance estimate updated - |

1||

I

P

PHKIP

kk

kkkkkk

kkkkkk yKxx ~ˆˆ 1||

k sample,at n observatio - z

residual)nt (measureme innovation - ~ˆ~

k

1|

k

kkkkk

y

xHzy

21

The Gravity Acceleration Model

Observation

ak is the linear and angular accelerations experienced due to physiological movement

gk is gravity

bk is the offset (bk = bk-1 – ) ( is a constant)

is the noise with potentially time varying covariance, A

zk is a 3x1 vector of Cartesian coordinates

The unknown states are ak, gk, and bk

Its very complicated because all three are unknown

kAkkkk vbgaz

kAv

22

Modeling Options

Case 1: Simplified ModelJust Gravity with a simplified prediction model

x(k) = x(k-1)

Case 2: Linear Extrapolation ModelJust Gravity linearly extrapolated from past

two estimatesSlope between x(k-1) and x(k-2) is equal to slope

between x(k) and x(k-1)

23

Case 1 Model

100

010

001

H

zk

yk

xk

k

g

g

g

gkkk

kkk

wxx

ugz

1

100

010

001

2F

24

Case 2 Model Acceleration, offset and noise were combined for

this model

k

zk

yk

xk

zk

yk

xk

zk

yk

xk

zk

yk

xk

x

x

xx

x

x

F

x

x

xx

x

x

w

2

2

2

1

1

1

1

1

1

kkkk

kkk

wxxx

ugz

212

000

000

000

100

010

001100

010

001

200

020

002

1F

100

010

001

H

25

Simulation Results Gravity

Generated test data from polar

coordinates Converted test data to Cartesian coordinates

Modeling Errors Added AWGN with SNR ranging from 0 – 60 dB A small constant offset was added as well Accelerations were added by varying theta and phi

Q = 10-6

Modeling error constant Varied modeling error to investigate the modeling

error effects

s

s

polark

fff

famplitude

g

2sin

2sin

0.1

26

X Component - Model 1

0dB 15dB 30dB

45dB 60dB

27

Gravity X Component - Model 2

0dB 15dB 30dB

45dB 60dB

28

Impact of Offset vs. Modeling Error When the SNR is high, the offset becomes

the dominating error = 1/(1,000)

29

MSE Plots Comparing Models

30

Modeling Error vs. MSE – Case 1

31

Work Conclusion

An accelerometer can feasibly be used to estimate physiological human motion

For complex estimates, a Kalman filter may a feasible method to estimate fine physiological states like slouching A more accurate model may be needed (and is in

development) Other sensors like gyroscopes and

magnetometers are unnecessary

32

Future Work

More Accurate Models Use more accurate physics in modeling movement Model Depth – (i.e. FIR Filter) Determine Linearity of Signals and Distribution of

Noise If model is nonlinear, a Particle Filter is a viable

option Synthesizable RTL Implementation

Low Power Architectures for Implantable Systems

Thank You

Estimation and Classification of

Human Movement Using 3 Axis Accelerometers