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Updated: Jun 22, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Decoding of individuated finger movements using surface electromyography.

Francesco V G Tenore1, Ander Ramos, Amir Fahmy

  • 1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. francesco.tenore@jhuapl.edu

IEEE Transactions on Bio-Medical Engineering
|May 29, 2009
PubMed
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Noninvasive myoelectric signals accurately decode individual finger movements in transradial amputees, enabling advanced prosthetic hand control. This breakthrough shows no significant difference compared to able-bodied individuals, paving the way for dexterous prosthetics.

Area of Science:

  • Biomedical Engineering
  • Neuroprosthetics
  • Rehabilitation Engineering

Background:

  • Upper limb prostheses are advancing in form and function, mimicking natural limbs.
  • Controlling multifingered prosthetic hands requires managing numerous degrees of freedom (DOFs).
  • Existing noninvasive control methods are often limited to simpler prosthetic configurations.

Purpose of the Study:

  • To investigate the feasibility of decoding individual finger movements using noninvasive surface myoelectric signals.
  • To assess control accuracy for ten distinct finger movements (flexion/extension) in a transradial amputee.
  • To compare decoding accuracy between amputee and able-bodied subjects for potential real-time prosthetic control.

Main Methods:

  • Utilized noninvasive surface myoelectric signals recorded from a transradial amputee.

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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

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Last Updated: Jun 22, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Published on: March 28, 2025

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

  • Developed a decoding strategy to interpret signals corresponding to individual finger flexion and extension.
  • Compared decoding accuracy between the amputee and able-bodied participants.

    • Main Results:

    • Achieved greater than 90% accuracy in decoding individual finger flexion and extension movements.
    • Demonstrated no statistically significant difference in decoding accuracy between the transradial amputee and able-bodied subjects (p < 0.05).
    • Validated the potential of surface myoelectric signals for complex prosthetic hand control.

    Conclusions:

    • Noninvasive surface myoelectric signals can effectively decode individual finger movements for prosthetic control.
    • The decoding accuracy in transradial amputees is comparable to that of able-bodied individuals.
    • These findings support the development of advanced, real-time control strategies for dexterous prosthetic hands.