Swedish Research Council project grant: machine learning and personalisation of rehabilitation
Annika Waern and Laia Turmo Vidal have been granted a Swedish Research Council (Vetenskåpsradet) project grant to investigate how machine learning can support the personalisation of out-of-clinic rehabilitation. This research builds directly on prior research in the Human-Computer Interaction group at the Department of Informatics and Media regarding open-ended tools for instructed physical training, and combines it with approaches to interactive machine learning to make tools adaptable to the individual.
Body movement is our primary means of interacting with the world, and in future sensor-based modes of interaction, this will include artificial intelligence (AI) based functions. This makes research into the design of movement-based AI critical, as technical systems literally shape users. In physiotherapy, technology as a support for physiotherapy has been investigated for its potential of providing guidance and feedback in exercises to do at home. While such exercises typically have normative stances on what is considered correct, in practice both what is considered correct, and how corrections are made, is situationally dependent. Current solutions often suffer from recognizing only a very limited set of movements, or limit who can use the systems.
The goal of the project is to empower both physiotherapists and patients to take control over their tools, and adapt them to suit their specific needs. Of central concern is 1) maintaining the fluent concept of what is considered a ‘correct’ exercise execution and 2) to develop exploration mechanisms to control the machine learning based learned representations, to ensure that physiotherapists and patients can tailor them to their practice.
The project is expected to start within 2021 and run for three years.
Annika Waern, professor at the Department of Informatics and Media, Uppsala University.
Laia Turmo Vidal, PhD student at the Department of Informatics and Media, Uppsala University.
Interactive Machine Learning for Personalised Physical Training – project web page (Link removed) .