Empowering stroke recovery with upper limb rehabilitation monitoring using TinyML based heterogeneous classifiers

Xie, J., Wu, Q., Dey, N., Shi, F., Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 and Kuang, Y. (2025) Empowering stroke recovery with upper limb rehabilitation monitoring using TinyML based heterogeneous classifiers. Scientific Reports, 15. 18090. ISSN 2045-2322 doi: 10.1038/s41598-025-01710-y

Abstract/Summary

Stroke is one of the leading causes of disability worldwide, with approximately 70% of survivors experiencing motor impairments in the upper limbs, significantly affecting their quality of life. Home-based rehabilitation offers a cost-effective approach to improving motor function, but it faces challenges, including inaccurate movement reporting, lack of real-time feedback, and the high cost of rehabilitation equipment. Therefore, there is a need for affordable, lightweight home-based rehabilitation monitoring systems. This paper presents an intelligent wearable sensor system that utilizes TinyML AI technology to classify eight upper limb rehabilitation movements with minimal sensors. The system is designed for patients with upper limb impairments who retain antigravity voluntary movement, enabling them to monitor rehabilitation progress at home. The study recruited 10 healthy volunteers to perform rehabilitation movements, creating a standardized dataset for model training. Data normalization, preprocessing, model training, and deployment were carried out using the Edge Impulse platform. A hybrid classifier, combining multilayer perceptron and k-means clustering, achieved 96.1% training accuracy, 95.09% testing accuracy, and 88.01% deployment accuracy. The proposed TinyML-based system shows promising potential for home-based rehabilitation of stroke patients.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/122851
Identification Number/DOI	10.1038/s41598-025-01710-y
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	Nature Publishing Group
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