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 interpret 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: A= a 2x a 2y a 2z
Directional angles using Cartesian Coordinates:
= arccos a x / A , = arccos a y / A , = arccos a z / A
D is the combined coordinates in all three directions:
cos = − d z / D , d x = Dcos
, d x = Dcos
Quantitative Features
•
Total Distance: D t = ∑
n= startpoint
endpoint
dy − dy
n
2
n 1
dx − dx
n
2
n 1
•
Mean Speed: s m= D t /t
•
2
r
=
1/
N
d
Mean Radius: m
∑ n= startpoint x
•
Mean Frequency: f m= D t / 2
•
Anterior/Posterior Displacement: d a/ p = max d d − min d d
endpoint
n
d 2y
n
rm
xn
∀ n
∀ n
Medial/Lateral Displacement: d m/ l = max d d − min d d
∀ n
yn
∀ n
yn
xn
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
–

Parameter Extraction
–

Accelerometer & EMG signals recorded every 4
seconds
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