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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Phase-Based Grasp Classification for Prosthetic Hand Control Using sEMG.

Shuo Wang1, Jingjing Zheng1, Bin Zheng2

  • 1Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1C 5S7, Canada.

Biosensors
|February 24, 2022
PubMed
Summary
This summary is machine-generated.

Researchers identified an optimal "sweet period" during object grasping for prosthetic hand control using surface Electromyography (sEMG). This allows for earlier grasp detection and reduced delay in prosthetic hand gestures.

Keywords:
grasp classificationgrasp phases analysismachine learningmyoelectric prosthesissEMG

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Area of Science:

  • Biomedical Engineering
  • Neuroprosthetics
  • Human-Computer Interaction

Background:

  • Surface Electromyography (sEMG) is crucial for pattern recognition in prosthesis control.
  • Current methods often rely on the stable sEMG signal during the firmly grasped period, leading to control delays.
  • There is a need for earlier grasp detection to improve prosthetic hand responsiveness.

Purpose of the Study:

  • To investigate grasp classification accuracy during the dynamic reaching and grasping process.
  • To identify an optimal time window for early grasp detection with reduced latency.
  • To explore training strategies for real-time sEMG-based prosthetic control.

Main Methods:

  • Analyzed sEMG signal patterns during the entire reaching and grasping motion.
  • Evaluated grasp classification accuracy across different phases of the grasping process.
  • Identified a 'sweet period' preceding the firmly grasped phase for optimal classification.

Main Results:

  • Grasp classification accuracy progressively increased as the hand moved from reaching to firmly grasping.
  • A distinct 'sweet period' was identified before the firmly grasped phase, offering high accuracy and earlier detection.
  • This 'sweet period' enables reduced delay in prosthetic hand gesture control.

Conclusions:

  • The pre-grasping 'sweet period' is suitable for early and accurate grasp classification in sEMG-controlled prostheses.
  • Utilizing this period can significantly reduce the control delay associated with traditional firmly grasped period analysis.
  • Optimized training strategies are essential for effective real-time application of this early detection method.