Segmentation of exercise repetitions enabling real-time patient analysis and feedback using a single exemplar

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Sarsfield, J. ORCID: 0000-0003-0146-9102, Brown, D. ORCID: 0000-0002-1677-7485, Sherkat, N. ORCID: 0000-0003-1488-5682, Langensiepen, C. ORCID: 0000-0002-0165-9048, Lewis, J. ORCID: 0000-0002-2788-5043, Taheri, M. ORCID: 0000-0001-7594-4530, Selwood, L., Standen, P. and Logan, P., 2019. Segmentation of exercise repetitions enabling real-time patient analysis and feedback using a single exemplar. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27 (5), pp. 1004-1019. ISSN 1558-0210

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Official URL: https://doi.org/10.1109/TNSRE.2019.2907483

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:

Journal article

Publication Title:

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Creators:

Sarsfield, J., Brown, D., Sherkat, N., Langensiepen, C., Lewis, J., Taheri, M., Selwood, L., Standen, P. and Logan, P.

Publisher:

Institute of Electrical and Electronics Engineers

Date:

May 2019

Volume:

Number: