Towards Interactive ML for Personalised Physical Training

Laia Turmo, (KTH) och Annika Waern, professor at Department of Informatics and Media, Uppsala university, presents: Towards Interactive Machine Learning for Personalised Physical Training.

Join on ZOOM: https://umu.zoom.us/j/67389473665

Abstract: Body movement is our primary means of interacting with the world, and in future sensor-based modes of interaction, this will include AI-based functions. This makes research into the design of movement-based AI critical, as technical systems literally shape users. The issue comes at an edge 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.  Current solutions often suffer from recognising only a very limited set of movements, or limit who can use the systems. In a VR-funded research project, MovAI, we explore how machine learning can support the personalisation of in- and out-of-clinic training. Our research combines current research into open-ended sensory stimuli for physical training with approaches to interactive machine learning - to make tools adaptable to the individual. In this talk, we present the project MovAI, reflect on our current results and paths forward towards developing interactive machine learning tools for personalised physical training - with the ultimate goal of empowering both physiotherapists and patients to take control over their tools and adapt them to suit their specific needs.