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EMG based classification for pick and place task.

Salman Mohd Khan1, Abid Ali Khan1, Omar Farooq1

  • 1Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India.

Biomedical Physics & Engineering Express
|April 21, 2021
PubMed
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This study analyzed electromyography (EMG) signals during pick and place tasks for hand amputees. The k-nearest neighbor (k-NN) classifier best identified movement phases and joint deviations, aiding assistive device development.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Hand amputees face challenges in daily living due to loss of function.
  • Understanding forearm muscle activity patterns is crucial for developing effective assistive devices.
  • The pick and place task is a fundamental activity involving complex hand and wrist movements.

Purpose of the Study:

  • To classify electromyography (EMG) signals during pick and place actions.
  • To differentiate phases of activity, grip force, and joint deviation.
  • To evaluate machine learning classifiers for assistive device pattern recognition.

Main Methods:

  • Developed a force-measuring gripper to simulate pick and place tasks with varying grip spans (6-9 cm) and weights (up to 750 gms).
Keywords:
classificationelectromyographyfeature extractionpick and place task

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  • Recorded EMG signals from forearm muscles during these tasks.
  • Applied pattern recognition using Decision Tree (DT), Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN) classifiers.

    • Main Results:

    • k-NN achieved 82% accuracy in classifying pick and place activity phases and 91% accuracy in classifying metacarpal phalangeal (MCP) joint deviation.
    • SVM demonstrated superior performance in classifying grip force with a specific feature set.
    • Classifier performance varied based on the specific feature sets extracted from EMG signals.

    Conclusions:

    • Machine learning classification of EMG signals is effective for understanding hand movements in pick and place tasks.
    • k-NN and SVM show promise for developing sophisticated pattern recognition in prosthetic or assistive devices.
    • Findings contribute to the design of advanced assistive technologies for hand amputees.