Towards Personalized Physiotherapy through Interactive Machine Learning: A Conceptual Infrastructure Design for In-Clinic and Out-of-Clinic Support

要旨

Machine learning (ML) is increasingly used in healthcare practices, due to its potential to support personalization, diagnostic and prediction, automatization, and increase effectiveness. In physiotherapy, most existing ML solutions suggest replacing the physiotherapist, neglecting the complexity of their skills and practice. We articulate an alternative to the design of ML technology for physiotherapy: one that emphasizes the relational aspects of the practice and offers personalized support to physiotherapists and patients alike. Based on domain studies and design explorations with physiotherapists, interaction designers and ML experts, we present 1) insights on physiotherapy's in-clinic and out-of-clinic looped structure, 2) opportunities and requirements to integrate ML in that loop, and 3) a conceptual interactive ML-based infrastructure that exploits those opportunities. Our work widens current ML developmental aims for physiotherapy, proposing a vision that encodes sustainable sociotechnical relationships in healthcare practices.

著者
Laia Turmo Vidal
KTH Royal Institute of Technology, Stockholm, Sweden
Annika Waern
Dept of Informatics and Media, Uppsala, Sweden
Rosa Cabanas-Valdés
Universitat Internacional de Catalunya, Barcelona, Spain
Lauren van Loo
Uppsala University, Uppsala, Sweden
Yinchu Li
Eindhoven University of Technology, Eindhoven, Netherlands
Karthik Venkataraman Meenaakshisundaram
Uppsala University, Uppsala, Sweden
DOI

10.1145/3706598.3713823

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713823

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Lifetime Digital Health

G316+G317
7 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
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