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Related Concept Videos

Muscles of the Forearm that Move the Hand and Fingers01:16

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The muscles of the forearm that move the wrist, hand, and digits are numerous and diverse. They can be classified into two groups based on their location and function — the anterior and posterior compartment muscles.
Anterior Compartment
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Hand motion recognition based on forearm deformation measured with a distance sensor array.

Sung-Gwi Cho, Masahiro Yoshikawa, Kohei Baba

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel hand motion recognition method using forearm deformation, achieving 92.6% accuracy. This technique shows potential for detecting deep muscle activity, overcoming limitations of surface electromyogram (sEMG) signals.

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    Area of Science:

    • Biomechanics
    • Robotics
    • Human-Computer Interaction

    Background:

    • Surface electromyogram (sEMG) signals are crucial for upper limb motion analysis in applications like robotic control.
    • sEMG signals primarily capture superficial muscle activity, lacking information on deep muscle engagement.
    • Forearm deformation during hand motion may provide insights into deep muscle activities.

    Purpose of the Study:

    • To develop a hand motion recognition method utilizing forearm deformation.
    • To investigate the potential of forearm deformation for sensing deep muscle activity.
    • To evaluate the accuracy of the proposed method for recognizing various hand motions.

    Main Methods:

    • Measurement of forearm deformation using a distance sensor array.
    • Application of a support vector machine (SVM) classifier for hand motion recognition.
    • Validation of the method across seven distinct hand motions.

    Main Results:

    • The proposed method achieved a mean accuracy of 92.6% for recognizing seven hand motions.
    • High accuracy was observed for pronation and supination recognition.
    • The distance sensor array demonstrated potential in estimating deep muscle activities.

    Conclusions:

    • Forearm deformation measurement is a viable approach for hand motion recognition.
    • This method offers a promising alternative to sEMG for capturing deeper muscle information.
    • The technology holds potential for advanced prosthetic and exoskeleton control systems.