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A Hybrid 3D Printed Hand Prosthesis Prototype Based on sEMG and a Fully Embedded Computer Vision System.

Maria Claudia F Castro1, Wellington C Pinheiro2, Glauco Rigolin1

  • 1Electrical Engineering Department, Centro Universitário FEI, São Bernardo do Cambo, Brazil.

Frontiers in Neurorobotics
|February 10, 2022
PubMed
Summary

This study introduces a 3D printed hand prosthesis using computer vision (CV) and a convolutional neural network (CNN) to recognize objects and select appropriate grasps. This hybrid system achieves high accuracy, improving prosthetic functionality.

Keywords:
3D printedcomputer visionconvolutional neural networkhand prosthesismyoelectric signal

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Computer Vision

Background:

  • Traditional prosthetic control methods often lack intuitive grasp selection.
  • The integration of advanced sensing and processing is crucial for enhancing prosthetic functionality.

Purpose of the Study:

  • To develop and evaluate a novel hybrid approach for surface electromyography (sEMG) hand prosthesis control.
  • To implement a computer vision (CV) system for object recognition and automatic grasp pattern determination.
  • To integrate CV with sEMG signals for intuitive prosthetic hand movement control.

Main Methods:

  • A 3D printed hand prosthesis model was developed with an embedded CV system.
  • A modified VGG-CNN on a Raspberry Pi 3 processed webcam imagery for object recognition.
  • Surface electromyography (sEMG) signals, processed via a finite state machine, initiated the CV grasp classification.
  • Keras and TensorFlow were utilized for network implementation and computation.

Main Results:

  • The CV system successfully classified five distinct grasp/gesture patterns with high accuracy.
  • The integrated system achieved 99% accuracy, 97% sensitivity, and 99% specificity.
  • The system demonstrated effective command of prosthetic motors for executing intended movements.

Conclusions:

  • The proposed hybrid CV-sEMG system offers a promising advancement in prosthetic hand control.
  • Computer vision technology can significantly enhance the grasp pattern definition in prosthetic devices.
  • This approach provides a more intuitive and functional control strategy for hand prostheses.