Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

Source: CHI Conference on Human Factors in Computing Systems

DOI: 10.1145/3706598.3713823

Authors: Laia Turmo Vidal, Annika Waern, Rosa Cabanas-Valdés, Lauren van Loo, Yinchu Li, Karthik Venkataraman Meenaakshisundaram

Institutions: KTH Royal Institute of Technology, Uppsala University, Universitat Internacional de Catalunya, Eindhoven University of Technology

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: Vetenskapsrådet