Personalization of home rehabilitation training by incorporating interactive machine learning into the design

Abstract: Home rehabilitation training has become an important part for patients to recover and maintain physical conditions due to the high health care cost and limited supervision in the clinic. Various technologies have been designed for assisting rehabilitation training but most of them are not able to provide personalized feedback and support according to different standards of patients’ physical condition and movement capability. The thesis aims to explore what information provided by the technology would be helpful for personalizing rehabilitation by incorporating interactive machine learning as part of a large research project, which has been discussed as an effective tool in motion interaction design to build conversation and provide personalized information. The participatory design methodology was conducted with bodystorming and role-playing approach in the workshops to collect people’s opinions on the role of technology, the design requirements and the way to present personalized feedback in rehabilitation training. The author collaborated with the research group to apply thematic analysis in the analysis of the workshop videos and drew the design spaces for future interaction design including three roles to integrate technology, five design concepts and some design takeaways to present feedback. Two interactive prototypes were envisioned based on the analysis result as an explorative design to incorporate the interplay between patients and machine learning in rehabilitation training.