Abstract
There is a lack of professional rehabilitation therapists and facilities in low-resource settings such as Bangladesh. In particular, the restrictively high costs of rehabilitative therapy have prompted a search for alternatives to traditional in-patient/out-patient hospital rehabilitation moving therapy outside healthcare settings. Considering the potential for home-based rehabilitation, we implemented a low-cost wearable system for 5 basic exercises namely, hand raised, wrist flexion, wrist extension, wrist pronation, and wrist supination, of upper limb (UL) rehabilitation through the incorporation of physiotherapists’ perspectives. As a proof of concept, we collected data through our system from 10 Bangladeshi participants: 9 researchers and 1 undergoing physical therapy. Leveraging the system’s sensed data, we developed a diverse set of machine learning models. And selected important features through three feature selection approaches: filter, wrapper, and embedded. We find that the Multilayer Perceptron classification model, which was developed by the embedded method Random Forest selected features, can identify the five exercises with a ROC-AUC score of 98.2% and sensitivity of 98%. Our system has the potential for providing real-time insights regarding the precision of the exercises which can facilitate home-based UL rehabilitation in resource-constrained settings.
Md. S. Ahmed and S. Amir—Equal contribution.
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Acknowledgment
We thank Dr. Catt Turney for input in the physiotherapist research, and all participants for their time. This study was supported by seed funding from Cardiff University’s GCRF QR Funding from the Higher Education Funding Council for Wales.
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Ahmed, M.S. et al. (2023). A Low-Cost Wearable System to Support Upper Limb Rehabilitation in Resource-Constrained Settings. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_3
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