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Related Experiment Video

Updated: Dec 9, 2025

Force and Position Control in Humans - The Role of Augmented Feedback
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Online Grasp Force Estimation From the Transient EMG.

Itzel Jared Rodriguez Martinez, Andrea Mannini, Francesco Clemente

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |September 7, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Researchers decoded grasp force using the transient phase of electromyogram (EMG) signals. This new method allows for intuitive control of prosthetic hands, improving biomimetic regulation and reducing user effort during grasping tasks.

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    Area of Science:

    • Biomedical Engineering
    • Neuroprosthetics
    • Rehabilitation Robotics

    Background:

    • Myoelectric prostheses utilize electromyogram (EMG) signals from residual muscles for control.
    • EMG patterns during muscle contraction onset (transient phase) contain predictive information about intended grasps.
    • Decoding transient EMG for grasp force estimation has been an underexplored area.

    Purpose of the Study:

    • To translate findings on EMG transient phase information for grasp force estimation into an online platform.
    • To evaluate the online platform's performance with both non-amputee and amputee participants.
    • To enable biomimetic grasp force regulation in prosthetic hands.

    Main Methods:

    • Developed an online platform based on offline study findings for real-time EMG analysis.
    • Tested the platform during a pick-and-lift task using light objects requiring fine force control.
    • Utilized the transient phase of EMG signals to estimate target grasp force.

    Main Results:

    • Accurate estimation of target grasp force was achieved with low absolute errors (2.06% for non-amputees, 2.04% for amputees) of maximum voluntary force.
    • The transient phase of EMG successfully predicted grasp force during the tested task.
    • The system demonstrated feasibility for online application in prosthetic control.

    Conclusions:

    • The transient phase of EMG signals contains sufficient information for accurate grasp force estimation in myoelectric prostheses.
    • This approach allows for a single muscle contraction to set the grasp force, mimicking natural control.
    • The findings support the development of faster, more intuitive, and robust myoelectric control systems for upper limb prostheses.