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Locomotion mode classification using a wearable capacitive sensing system.

Baojun Chen, Enhao Zheng, Xiaodan Fan

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 23, 2013
    PubMed
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    A new wearable capacitive sensing system accurately classifies locomotion modes for prosthetic limb control. This technology offers a promising alternative to electromyography (EMG) systems, demonstrating comparable performance and real-time decision-making capabilities.

    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Wearable Sensors

    Background:

    • Locomotion mode classification is crucial for controlling powered lower-limb prostheses.
    • Electromyography (EMG)-based systems are popular but have drawbacks.
    • Alternative sensing methods are needed for improved prosthetic control.

    Purpose of the Study:

    • To propose and evaluate a wearable capacitive sensing system for locomotion mode recognition.
    • To compare the performance of capacitive sensing with traditional EMG-based systems.
    • To assess the suitability of capacitive sensing for real-time control of lower-limb prostheses.

    Main Methods:

    • Recruited eight able-bodied subjects and five transtibial amputees.
    • Measured ten channels of capacitance signals from the shank, thigh, or both.

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  • Utilized a phase-dependent linear discriminant analysis classifier with time-domain features.

    • Main Results:

    • Achieved high classification accuracy: 93.6% ±0.9% for able-bodied and 93.4% ±0.8% for amputees.
    • Demonstrated comparable accuracy to EMG-based systems.
    • Verified that capacitive sensing does not impede timely classification decisions, even with neuro-mechanical delay.
    • Showed that thigh-based capacitance signals alone are sufficient for classification.

    Conclusions:

    • Wearable capacitive sensing is a viable and promising alternative to myoelectric sensing for powered lower-limb prostheses.
    • The system offers comparable accuracy and timely decision-making to EMG systems.
    • Thigh-based capacitive sensing provides a simplified yet effective approach for locomotion mode classification.