Segmentation of Exercise Repetitions Enabling Real-Time Patient Analysis and Feedback Using a Single Exemplar

SARSFIELD, Joe, BROWN, David, SHERKAT, Nasser, LANGENSIEPEN, Caroline, LEWIS, James, TAHERI, Mohammad, SELWOOD, Louise, STANDEN, Penny and LOGAN, Pip (2019). Segmentation of Exercise Repetitions Enabling Real-Time Patient Analysis and Feedback Using a Single Exemplar. IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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Link to published version:: https://doi.org/10.1109/TNSRE.2019.2907483
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    Abstract

    We present a segmentation algorithm capable of segmenting exercise repetitions in real-time. This approach uses subsequence dynamic time warping and requires only a single exemplar repetition of an exercise to correctly segment repetitions from other subjects, including those with limited mobility. This approach is invariant to low range of motion, instability in movements and sensor noise while remaining selective to different exercises. This algorithm enables responsive feedback for technology-assisted physical rehabilitation systems. We evaluated the algorithm against a publicly available dataset (CMU) and against a healthy population and stroke patient population performing rehabilitation exercises captured on a consumer-level depth sensor. We show the algorithm can consistently achieve correct segmentation in real-time.

    Item Type: Article
    Identification Number: https://doi.org/10.1109/TNSRE.2019.2907483
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 16 Apr 2019 09:44
    Last Modified: 02 Jul 2020 11:34
    URI: http://shura.shu.ac.uk/id/eprint/24445

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