Signal Processing Feature Analysis

pancakesbootAI and Robotics

Nov 24, 2013 (3 years and 10 months ago)

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Body Sensor Networks to Evaluate Standing
Balance: Interpreting Muscular Activities
Based on Intertial Sensors

Rohith Ramachandran

Lakshmish Ramanna

Hassan Ghasemzadeh

Gaurav Pradhan

Roozbeh Jafari

Balakrishnan Prabhakaran

University of Texas at Dallas


Presented by,

Corey Nichols


Introduction


Why i
nterpret muscle activities for balance
performance based on intertial sensors
?


Rehabilitation, sports medicine, gait analysis, & fall
detection all can make use of a balance evaluation.


Inertial sensors currently in use, but do not measure
muscle activity directly


Measuring muscle activity may provide additional info


Goal


Investigate EMG signals to interpret standing balance


Use inertial sensors to help interpret these signals


Balance Parameters


[1] Mayagoitia, R.E., et al., Standing balance
evaluation using a triaxial accelerometer. Gait and
Posture, 2002. 16: p.55
-
59.


Parameters are classified as low, medium, and high


Want to analyze EMG signals to make the same
classifications using Linear Discriminant Analysis
(LDA)


LDA: Method in statistics and machine learning to find a
linear combination of features that best separates
multiple classes of objects or events
(source: wikipedia)

Evaluation Model


Uses the Balance Evaluation Model from [1]


Uses a single accelerometer


Height of the center of mass


Build and trace an acceleration vector


Building and tracing an
Acceleration vector

Building and tracing an
Acceleration vector


Combined Acceleration:


Directional angles using Cartesian Coordinates:



D is the combined coordinates in all three directions:


A
=
a
x
2
a
y
2
a
z
2
=
arccos
a
x
/
A
,
=
arccos
a
y
/
A
,
=
arccos
a
z
/
A
cos
=

d
z
/
D
,
d
x
=
Dcos
,
d
x
=
Dcos
Quantitative Features


Total Distance:


Mean Speed:


Mean Radius:


Mean Frequency:


Anterior/Posterior Displacement:


Medial/Lateral Displacement:

D
t
=

n
=
startpoint
endpoint
d
y
n

d
y
n
1
2
d
x
n

d
x
n
1
2
s
m
=
D
t
/
t
r
m
=
1
/
N

n
=
startpoint
endpoint
d
x
n
2
d
y
n
2
f
m
=
D
t
/
2
r
m
d
a
/
p
=
max

n
d
d
x
n

min

n
d
d
x
n
d
m
/
l
=
max

n
d
d
y
n

min

n
d
d
y
n
Quantitative Features

System Architecture


Inertial Sensor Subsystem


EMG Sensor Subsystem


Balance Platform


Inertial Sensor Subsystem


Body sensor network of two nodes


A tri
-
axial 2g accelerometer


Samples at 40Hz


Base station


Collects data over wireless

channel


Relays info to PC via USB


Sensor data is collected and

processed using MATLAB


EMG Sensor Subsystem


Four EMG sensors used


Measures electric activity

generated by muscle

contractions


Electrodes acquire EMG signal


Sample at 1000Hz


Signal is amplified and

band
-
pass filtered to 20
-
450Hz


Data is transferred to a PC and processed off line



Balance Platform


Balance ball (half sphere w/ standing platform)


Use a level to control

the experiment or for

coaching

Signal Processing Feature
Analysis


Five stages of operation


Data Collection


Parameter Extraction


Quantization


Feature Extraction on

EMG


Feature Analysis

Signal Processing Feature
Analysis


Data Collection


Accelerometer & EMG signals recorded every 4
seconds


Parameter Extraction


Extract 5 quantization factors using the accelerometer
data


Quantization


Classify data into 'low', 'medium' and 'high


Within 1
std. Dev. of the mean implies 'medium'

Signal Processing Feature
Analysis


Feature Extraction on EMG


Obtain an exhaustive set of statistical features from the
EMG signals


Signal Energy, Maximum Peak, Number of Peaks, Avg.
Peak Value, and Average Peak rate


Feature Analysis


Using LDA, extract significant features from EMG
signals


Determine if the EMG signals are representative of the
quantitative features for balance evaluation from the
accelerometer

Experimental Procedure


Subjects:


5 males aged 25
-
32 and 1.65
-
1.8m tall with no
disorders


Wore the accelerometer on a belt around the waist with
the sensor positioned in the back.


4 EMG electrodes attached on the lower leg


Right/Left
-
Front (Tibalis Anterior muscle)


Right/Left
-
Back leg (Gastrocnemius muscle)

Experimental Procedure


Sensors
:


Delsys “Trigger Module” allows the EMG to work
sychronously with the accelerometer


MATLAB tool sends the trigger


To EMG through the trigger module


To accelerometer through USB


MATLAB tool analyzes the data


Data was recorded every 4 seconds

Experimental Procedure


Test Conditions:


Nine test conditions


Two trials per condition

Experimental Results


90 trials performed


Classifies each trial into 'low',

'medium', & 'high' qualities


Done for each accelerometer

parameter


Each EMG feature is

assigned the same quality

label as its corresponding

accelerometer

data

Experimental Results


Made EMG signals representative of performance
parameter for balance evaluation


Used 50% of trials to find significant features


The remaining trials were for evaluation of the system


Extracted 5 signals from each of the four EMG


Form a 20 dimensional space that is representative of
some muscle activity properties


LDA is used to select the most prominent feature from
the subset

Experimental Results


Uses the k
-
Nearest Neighbor classifier to determine
the effectiveness of the EMG features


K
-
NN classifies objects using

training examples

Questions?

Related Work


A lot of work has been done based on human
performance and quality of balance


A study on children compared EMG with
kinetic parameters for balance responses
shows that muscle activities contribute to
balance


This is the first work that uses inertial sensors
to help interpret EMG signals

Conclusion & Future Work


Uses acceleration and muscle activity data to
perform an analysis during standing balance


Break the accelerometer data down into five metrics


Prominent features are extracted from EMG signals
using the accelerometer data to evaluate the balance


Future goals:


Integrate a “gold standard balance system” with their
experiments


deploying a system that performs the data processing in
real
-
time