Real

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Real Time Locomotion Classification using
Transient Surface EMG signals

• Sarthak Pati1, Deepak Joshi2, Ashutosh Mishra2
  and Sneh Anand2
• 1 Dept. Of Biomedical Engineering, Manipal
  University
• 2 Center for Biomedical Engineering, IIT Delhi

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Contents

• Introduction to EMG and its acquisition
• Importance of EMG
• Pre Processing of EMG signals
• Features under consideration
• Classifier design

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What is EMG ?

• It is a signal used to evaluate the electrical
  activity produced by skeletal muscles.

Fig 1 EMG Signal of Healthy Subject
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Block Diagram
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EMG Surface Electrodes
Fig 2 EMG Surface Electrodes
Image Courtesy Orthotics and Prosthetics Lab,
BME Unit, AIIMS
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Electrode Placement
Fig 3 Electrode Placement Diagram
Image Courtesy Orthotics and Prosthetics Lab,
BME Unit, AIIMS
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Importance of EMG

• Diagnosis of
• Neuro - Muscular Disorders
• Motor Control Disorders
• Prosthetic Control

• Sensing of Isometric Motor Activity (motionless
  gestures)
• Flight control (Human Senses Group, NASA)
• MachineHuman Interfacing (Advanced Robotics,
  MIT)

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Why EMG for this study ?

• Relatively easy to acquire and process
• If properly utilised, gives good accuracy for
  control systems
• High sensitivity
• Single Muscle Recording Possible
• Access to Deep Musculature
• Little cross talk concern

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EMG Signal Processing
Fig 4 Frequency Response of Band Pass Filter
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Feature Selection

• Criteria
• Computational Efficiency
• High separability with respect to locomotion modes

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Classifier Design

• Obtaining LDA Transformation Matrix T
• Each Locomotion Mode mapped to a single dimension
  data set using T
• Threshold based approach for classification

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Results
Fig 5 LDA classification between all the four
locomotion modes
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Continued
Fig 6 LDA classification between FW and SW
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References

• Deepak Joshi, Sneh Anand - Study of circular
  cross correlation and phase lag to estimate knee
  angle an application to prosthesis Int. J.
  Biomechatronics and Biomedical Robotics in
  press
• Hargrove L. J., Huang H., Schultz A. E., Lock B.
  A., Lipschutz R., Kuiken T. A. - Toward the
  Development of a Neural Interface for Lower Limb
  Prosthesis Control Delsys Prize Winner
• Parker P., Englehart K., Hudgins B. - Myoelectric
  signal processing for control of powered limb
  prostheses Journal of Electromyography and
  Kinesiology
• Englehart K., Hudgins B. - A Robust, Real-Time
  Control Scheme for Multifunction Myoelectric
  Control IEEE Transactions on Biomedical
  Engineering, Vol.50, No.7
• Chan F.H.Y., Yang Y.S., Lam F.K., Zhang Y.T.,
  Parker P.A. - Fuzzy EMG Classification for
  Prosthesis Control IEEE Transactions on
  Rehabilitation Engineering, Vol.8, No.3
• Englehart K., Hudgins B., Parker P., Maryhelen S.
  - Time-Frequency Representation for
  Classification of The Transient Myoelectric
  Signal 20th Annual International Conference of
  the IEEE Engineering in Medicine and Biology
  Society, Vol. 20, No 5
• Phinyomark A., Limsakul C., Phukpattaranont P. -
  A Novel Feature Extraction for Robust EMG Pattern
  Recognition Journal of Computing, Vol 1, Issue
  1, ISSN 2151-9617

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Thank You

• Any questions?