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

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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

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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

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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)

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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

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Building and tracing an Acceleration vector
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Building and tracing an Acceleration vector

• Combined Acceleration
• Directional angles using Cartesian Coordinates
• D is the combined coordinates in all three
  directions

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Quantitative Features

• Total Distance
• Mean Speed
• Mean Radius
• Mean Frequency
• Anterior/Posterior DisplacementMedial/Lateral
  Displacement

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Quantitative Features
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System Architecture

• Inertial Sensor Subsystem
• EMG Sensor Subsystem
• Balance Platform

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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

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EMG Sensor Subsystem

• Four EMG sensors used
• Measures electric activitygenerated 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

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Balance Platform

• Balance ball (half sphere w/ standing platform)
• Use a level to controlthe experiment or
  forcoaching

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Signal Processing Feature Analysis

• Five stages of operation
• Data Collection
• Parameter Extraction
• Quantization
• Feature Extraction on EMG
• Feature Analysis

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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'

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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

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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)

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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

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Experimental Procedure

• Test Conditions
• Nine test conditions
• Two trials per condition

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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

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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

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Experimental Results

• Uses the k-Nearest Neighbor classifier to
  determine the effectiveness of the EMG features
• K-NN classifies objects usingtraining examples

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Questions?
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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

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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