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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands.

Alex K Vaskov1, Philip P Vu2, Naia North3

  • 1Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA.

IEEE Transactions on Robotics : a Publication of the IEEE Robotics and Automation Society
|May 16, 2023
PubMed
Summary

Researchers developed a new method for intuitive prosthetic hand control by directly extracting finger commands from the neuromuscular system using implanted electrodes. This approach enables faster, more accurate prosthetic grasp control for amputees.

Keywords:
finger and grasp controlintramuscular electrodesmyoelectric prosthesesperipheral nerve interfaces

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Area of Science:

  • Biomedical Engineering
  • Neuroprosthetics
  • Rehabilitation Robotics

Background:

  • Current prosthetic hands offer limited degrees of freedom (DOF), leading to unintuitive and difficult grasp control.
  • Directly translating neuromuscular signals into prosthetic commands remains a significant challenge in upper-limb prosthetics.

Purpose of the Study:

  • To investigate the efficacy of extracting finger commands directly from the neuromuscular system for intuitive prosthetic hand control.
  • To evaluate the performance of a high-speed movement classifier utilizing intramuscular electrodes and regenerative peripheral nerve interfaces (RPNIs).

Main Methods:

  • Two participants with transradial amputations received bipolar electrodes implanted into RPNIs and residual muscles.
  • Electromyography (EMG) signals were recorded and processed in real-time using a high-speed movement classifier to control a virtual prosthetic hand.
  • Participants performed tasks involving individual finger/wrist postures and functional grasp assessments.

Main Results:

  • High success rates (94.7% for 10 postures, 100% for 5 grasps) and low trial latencies (255 ms, 135 ms) were achieved in real-time prosthetic hand control.
  • Performance remained stable across different arm positions and when supporting prosthesis weight.
  • Participants successfully switched between robotic prosthetic grips and completed functional assessments.

Conclusions:

  • Pattern recognition systems employing intramuscular electrodes and RPNIs enable fast and accurate prosthetic grasp control.
  • Direct neuromuscular signal extraction offers a promising pathway for improving the intuitiveness and functionality of prosthetic devices.
  • This technology has the potential to significantly enhance the quality of life for individuals with upper-limb amputations.