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Deep learning-based artificial vision for grasp classification in myoelectric hands.

Ghazal Ghazaei1, Ali Alameer, Patrick Degenaar

  • 1School of Electrical and Electronic Engineering, Newcastle University, Newcastle-upon-Tyne NE1 7RU, United Kingdom.

Journal of Neural Engineering
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Summary
This summary is machine-generated.

This study introduces a computer vision system for trans-radial amputees to control prosthetic hands. The deep learning approach enables effective object grasping and manipulation, improving prosthetic functionality.

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Rehabilitation Technology

Background:

  • Sensorimotor disorders significantly impact quality of life.
  • Computer vision offers potential for advanced assistive technologies.
  • Myoelectric prosthetic hands require intuitive control systems.

Purpose of the Study:

  • To develop a computer vision system for trans-radial amputees.
  • To enable grasping and manipulation of household objects using a myoelectric prosthetic hand.
  • To improve the functionality of prosthetic hands through artificial vision.

Main Methods:

  • A deep learning-based artificial vision system using a convolutional neural network (CNN) was developed.
  • The CNN was trained to classify objects into four grasp patterns without explicit identification or dimension measurement.
  • The system was tested offline and in real-time with novel and rotated objects, and finally with trans-radial amputee volunteers.

Main Results:

  • Offline classification accuracy reached [Formula: see text] for seen and [Formula: see text] for novel objects.
  • Real-time classification accuracy was [Formula: see text] for a set of novel and rotated objects.
  • Trans-radial amputees achieved up to [Formula: see text] success rate in picking up and moving objects, with performance improving with training.

Conclusions:

  • Deep learning-based computer vision significantly enhances myoelectric prosthetic hand grip functionality.
  • This system offers a substantial conceptual improvement for controlling multi-functional prosthetic hands.
  • The study demonstrates the feasibility of using artificial vision for intuitive prosthetic control in amputees.