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Home-Based Monitor for Gait and Activity Analysis
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Body-Sensor-Network-Based Spasticity Detection.

Berno J E Misgeld, Markus Luken, Daniel Heitzmann

    IEEE Journal of Biomedical and Health Informatics
    |September 11, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for detecting spasticity using body-worn sensors and electromyography (EMG) signals. The developed algorithm quantifies muscle coactivation, aiding in the assessment and adjustment of rehabilitative motor training for spasticity.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Science
    • Neurology

    Background:

    • Spasticity is a prevalent skeletal muscle disorder impacting motor function.
    • Quantitative spasticity measures are crucial for effective rehabilitative therapy.
    • Existing assessment methods may lack objective quantification during motor tasks.

    Purpose of the Study:

    • To develop and validate a novel algorithm for spasticity detection using body sensor networks (BSN).
    • To quantify muscle coactivation as an indicator of spasticity during human locomotion.
    • To assess the performance of the new algorithm against clinical expert classifications.

    Main Methods:

    • Development of a new electromyography (EMG) sensor node integrated into a BSN.
    • Analysis of clinical gait data from patients with unilateral cerebral palsy.
    • Algorithm implementation using cross-correlation of antagonistic muscle EMG signals, weighted by a Blackman window and signal energy.
    • Qualitative comparison with the Modified Ashworth Scale.

    Main Results:

    • The developed algorithm effectively detects coactivation of antagonistic muscles associated with spasticity.
    • The coactivation index, weighted by signal energy, provides a quantitative measure.
    • The BSN-based detection shows good performance in qualitative comparison with expert-classified clinical data.

    Conclusions:

    • The novel algorithm offers a promising approach for objective, quantitative spasticity detection using BSN.
    • This technology can enhance the assessment of motor training and therapy adjustments.
    • Potential applications include robotic sensorimotor therapy to mitigate spasticity effects.