estimation and classification of human movement using 3 axis accelerometers eric cope advisors: dr....
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Estimation and Classification of
Human Movement Using 3 Axis Accelerometers
Eric CopeAdvisors:
Dr. Antonia Papandreou-Suppappola
Dr. Bahar Jalali-FarahaniMarch 30, 2009
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Motivation
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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
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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
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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
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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)
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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
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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
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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
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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
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dt
tdta
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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?
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These Accelerations as Experienced by the Human Body Linear
Gravity, standing to walking Angular
Bending over to pick up a pencilSpinning like a topDancing
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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
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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
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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
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Current Published Applications
Gesture Movement Detection – Wii Athletic Optimizations Adaptive Noise Canceling of ECG Signals Human Movement
Knee Unlock – FallingMonitoringHeart Movement – HFRate Response
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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
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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.
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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 -
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Residual (or Innovation):
Innovation Covariance:
Optimal Kalman Gain
Overview of Kalman Filtering: Update Updated State Estimate
Updated State Covariance
GainKalman Optimal -
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SHPK
covariance noisen observatio -
estimate covariance predicted -
covariance innovation -
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covariance estimate updated - |
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k sample,at n observatio - z
residual)nt (measureme innovation - ~ˆ~
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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
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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)
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Case 1 Model
100
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001
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zk
yk
xk
k
g
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Case 2 Model Acceleration, offset and noise were combined for
this model
k
zk
yk
xk
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xk
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000
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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
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X Component - Model 1
0dB 15dB 30dB
45dB 60dB
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Gravity X Component - Model 2
0dB 15dB 30dB
45dB 60dB
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Impact of Offset vs. Modeling Error When the SNR is high, the offset becomes
the dominating error = 1/(1,000)
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MSE Plots Comparing Models
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Modeling Error vs. MSE – Case 1
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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
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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