AUTHOR=Lao Bryan , Tamei Tomoya , Ikeda Kazushi TITLE=Data-Efficient Framework for Personalized Physiotherapy Feedback JOURNAL=Frontiers in Computer Science VOLUME=2 YEAR=2020 URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2020.00003 DOI=10.3389/fcomp.2020.00003 ISSN=2624-9898 ABSTRACT=

Physiotherapy is a labor-intensive process that has become increasingly inaccessible. Existing telehealth solutions overcome many of the logistical problems, but they are cumbersome to re-calibrate for the various exercises involved. To facilitate self-exercise efficiently, we developed a framework for personalized physiotherapy exercises. Our approach eliminates the need to re-calibrate for different exercises, using only few user-specific demonstrations available during collocated therapy. Two types of augmented feedback are available to the user for self-correction. The framework's utility was demonstrated for the sit-to-stand task, an important activity of daily living. Although further testing is necessary, our results suggest that the framework can be generalized to the learning of arbitrary motor behaviors.