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Related Experiment Video

Updated: Jul 7, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Vision-based segmentation of continuous mechanomyographic grasping sequences.

Natasha Alves1, Tom Chau

  • 1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada. natasha.alves@utoronto.ca

IEEE Transactions on Bio-Medical Engineering
|February 14, 2008
PubMed
Summary

This study introduces a computer vision method to segment mechanomyographic (MMG) signals from continuous recordings. This approach aids in separating individual muscle contractions for applications like training myoelectric prostheses.

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Signal Processing

Background:

  • Continuous mechanomyographic (MMG) signal acquisition is preferred for motor activity detection.
  • Segmenting continuous MMG data into individual contractions is a significant challenge.
  • Accurate segmentation is crucial for developing advanced prosthetic control systems.

Purpose of the Study:

  • To develop and validate a computer vision-based method for segmenting continuous MMG data streams.
  • To enable efficient separation of individual muscle contractions from complex recordings.
  • To facilitate the creation of large datasets for training myoelectric prostheses.

Main Methods:

  • Synchronized video recordings of functional grasp sequences and forearm MMG signals were acquired.

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Last Updated: Jul 7, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans
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Published on: January 15, 2018

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  • An automatic algorithm utilizing skin color detection, motion estimation, and template matching was developed.
  • The algorithm segmented MMG signals based on visual cues from synchronized video data.

    • Main Results:

    • The vision-based segmentation method achieved high accuracy (97.8%) for two grips and moderate accuracy (73%) for up to seven grips.
    • The system effectively differentiated between multiple grips and tolerated extraneous hand movements.
    • Automated contraction timing was within 173 ms of manual segmentation.

    Conclusions:

    • The proposed computer vision method offers an effective solution for segmenting continuous MMG signals.
    • This technique simplifies the processing of MMG data for individual contraction analysis.
    • The method shows promise for assembling large signal datasets essential for training advanced MMG-driven prostheses.