Real-time myoelectric decoding of individual finger movements for a virtual target task.

Ryan J. Smith, David Huberdeau, Francesco Tenore, Nitish V. Thakor

Research output: Contribution to journalArticle

Abstract

This study presents the development of a myoelectric decoding algorithm capable of continuous online decoding of finger movements with the intended eventual application for use in prostheses for transradial amputees. The effectiveness of the algorithm was evaluated through controlling a multi-fingered hand in a virtual environment. Two intact limbed adult subjects were able to use myoelectric signals collected from 8 bipolar electrodes to control four fingers in real-time to touch and maintain contact with targets appearing at various points in the flexion space of the hand. In these tasks, subjects achieved accuracies of 94% when target regions extended +/- 11.5 degrees about a target angle and 81% when the target region extended only +/- 5.75 degrees about the target angle. The real-time virtual system provides a practical and economic way to develop and train algorithms and amputee subjects using dexterous prostheses.

Original languageEnglish (US)
Pages (from-to)2376-2379
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2009
Externally publishedYes

Fingerprint

Fingers
Decoding
Amputees
Prosthetics
Prostheses and Implants
Hand
User-Computer Interface
Computer Systems
Touch
Virtual reality
Electrodes
Economics

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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abstract = "This study presents the development of a myoelectric decoding algorithm capable of continuous online decoding of finger movements with the intended eventual application for use in prostheses for transradial amputees. The effectiveness of the algorithm was evaluated through controlling a multi-fingered hand in a virtual environment. Two intact limbed adult subjects were able to use myoelectric signals collected from 8 bipolar electrodes to control four fingers in real-time to touch and maintain contact with targets appearing at various points in the flexion space of the hand. In these tasks, subjects achieved accuracies of 94{\%} when target regions extended +/- 11.5 degrees about a target angle and 81{\%} when the target region extended only +/- 5.75 degrees about the target angle. The real-time virtual system provides a practical and economic way to develop and train algorithms and amputee subjects using dexterous prostheses.",
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