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Lower-Limb Rehabilitation at Home: A Survey on Exercise Assessment and Initial Study on Exercise State Identification Toward Biofeedback

Lower-Limb Rehabilitation at Home: A Survey on Exercise Assessment and Initial Study on Exercise State Identification Toward Biofeedback

Seanglidet Yean, Bu-Sung Lee, Chai Kiat Yeo
Copyright: © 2021 |Volume: 13 |Issue: 2 |Pages: 12
ISSN: 1941-8663|EISSN: 1941-8671|EISBN13: 9781799860570|DOI: 10.4018/IJITN.2021040102
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MLA

Yean, Seanglidet, et al. "Lower-Limb Rehabilitation at Home: A Survey on Exercise Assessment and Initial Study on Exercise State Identification Toward Biofeedback." IJITN vol.13, no.2 2021: pp.12-23. http://doi.org/10.4018/IJITN.2021040102

APA

Yean, S., Lee, B., & Yeo, C. K. (2021). Lower-Limb Rehabilitation at Home: A Survey on Exercise Assessment and Initial Study on Exercise State Identification Toward Biofeedback. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 13(2), 12-23. http://doi.org/10.4018/IJITN.2021040102

Chicago

Yean, Seanglidet, Bu-Sung Lee, and Chai Kiat Yeo. "Lower-Limb Rehabilitation at Home: A Survey on Exercise Assessment and Initial Study on Exercise State Identification Toward Biofeedback," International Journal of Interdisciplinary Telecommunications and Networking (IJITN) 13, no.2: 12-23. http://doi.org/10.4018/IJITN.2021040102

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Abstract

Ageing causes loss of muscle strength, especially on the lower limbs, resulting in higher risk to injuries during functional activities. The path to recovery is through physiotherapy and adopt customized rehabilitation exercise to assist the patients. Hence, lowering the risk of incorrect exercise at home involves the use of biofeedback for physical rehabilitation patients and quantitative reports for clinical physiotherapy. This research topic has garnered much attention in recent years owing to the fast ageing population and the limited number of clinical experts. In this paper, the authors survey the existing works in exercise assessment and state identification. The evaluation results in the accuracy of 95.83% average classifying exercise motion state using the proposed raw signal. It confirmed that raw signals have more impact than using sensor-fused Euler and joint angles in the state identification prediction model.

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