Personalised stroke rehabilitation: An AI pipeline for exercise programmes using a co-designed decision support tablet application

Rajkumar, Thrisha, Koerner, Sarah, Pinto, Anika, Shakya, Regan, Pope, James, Trigo, Maria ORCID: https://orcid.org/0000-0001-6492-0955, Al-Nuaimi, Ali, Loizou, Michael, O'Hara, Kenton and Kumar, Praveen 2026. Personalised stroke rehabilitation: An AI pipeline for exercise programmes using a co-designed decision support tablet application. Presented at: HEALTHINF 2026, Marbella, Spain, 02-04 March 2026. Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies. SCITEPRESS - Science and Technology Publications, pp. 529-537. 10.5220/0014414300004070

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Abstract

Stroke rehabilitation requires personalised and continuously adapted exercise programmes, resulting in significant therapist involvement and is often impractical for patients recovering at home in community settings. This motivates the need for assistive tools and decision support systems to enhance efficiency and rehabilitation progress. This position paper presents an integrated pipeline combining a therapist-informed tablet application with artificial intelligence (AI) models to support therapists in decision-making. Co-designed with stroke therapists, human-computer interaction (HCI) researchers, AI experts, and persons with stroke (PwS), the application captures baseline and weekly reassessment data, including BBS, TUG, pain, perceived difficulty, and FITT prescriptions, across 4–6 week cycles to determine whether to progress, sustain, or regress exercises. To facilitate early model development, we created a clinically informed synthetic dataset (n = 336 sessions across 5 PwS profiles over 12 weeks) that simulates functional progression and therapist decision-making patterns. This dataset reflects key features identified through workshops with clinicians and PwS, capturing essential assessment metrics such as stroke characteristics, functional scores, therapist goals, patient feedback, exercise difficulty, repetitions, duration, body area, FITT parameters, and exercise recommendations. We trained and evaluated models to predict weekly progression decisions. Logistic regression achieved a weighted F1-score of 51.6%, while a multilayer perceptron reached 79.3\% and a decision tree 90.2%. Clinical data will be collected in the next stage of the project (5–8 PwS, 4–6 weeks) and integrated with the synthetic dataset using real–synthetic fusion. This work advocates AI-augmented tools for scalable, patient-centred community stroke rehabilitation, with future efforts exploring generative AI and clinical validation.

Item Type:	Conference or Workshop Item - published (Paper)
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	SCITEPRESS - Science and Technology Publications
Related URLs:	https://healthinf.scitevents.org/?y=2026
Last Modified:	16 Mar 2026 15:29
URI:	https://orca.cardiff.ac.uk/id/eprint/185794

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