Towards the control of individual fingers of a prosthetic hand using surface EMG signals.

Francesco Tenore, Ander Ramos, Amir Fahmy, Soumyadipta Acharya, Ralph Etienne-Cummings, Nitish V Thakor

Research output: Contribution to journalArticle

Abstract

The fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls. Traditional control schemes are only capable of providing 2 degrees of freedom, which is insufficient for dexterous control of individual fingers. We present a framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy. 32 surface-EMG electrodes were placed on the forearm of an able-bodied subject while performing individual finger movements. Using time-domain feature extraction methods as inputs to a neural network classifier, we show that 12 individuated flexion and extension movements of the fingers can be decoded with an accuracy higher than 98%. To our knowledge, this is the first instance in which such movements have been successfully decoded using surface-EMG. These preliminary findings provide a framework that will allow the results to be extended to non-invasive control of the next generation of upper-limb prostheses for amputees.

Original languageEnglish (US)
Pages (from-to)6146-6149
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 - 2007
Externally publishedYes

Fingerprint

Prosthetics
Fingers
Hand
Artificial Limbs
Upper Extremity
Amputees
Forearm
Feature extraction
Electrodes
Classifiers
Neural networks

ASJC Scopus subject areas

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

Cite this

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abstract = "The fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls. Traditional control schemes are only capable of providing 2 degrees of freedom, which is insufficient for dexterous control of individual fingers. We present a framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy. 32 surface-EMG electrodes were placed on the forearm of an able-bodied subject while performing individual finger movements. Using time-domain feature extraction methods as inputs to a neural network classifier, we show that 12 individuated flexion and extension movements of the fingers can be decoded with an accuracy higher than 98{\%}. To our knowledge, this is the first instance in which such movements have been successfully decoded using surface-EMG. These preliminary findings provide a framework that will allow the results to be extended to non-invasive control of the next generation of upper-limb prostheses for amputees.",
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AU - Ramos, Ander

AU - Fahmy, Amir

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AU - Etienne-Cummings, Ralph

AU - Thakor, Nitish V

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