Virtual-Physio: A Virtual Assistant for Home Physiotherapy Rehabilitation

Background and Objective: Mobility impairments reduce the ability of patients to complete daily activities. Physio-therapeutic exercises help patients address such limitations or even completely recover. Correctly executing exercises is crucial to achieving good results; therefore, a physiotherapist's presence is essential to guide the execution of rehabilitation movements. In this context, the potential of combining advanced sensors with artificial intelligence techniques can represent a real breakthrough for home rehabilitation, as remote monitoring systems allow patients to avoid moving from home, making rehabilitation sessions less burdensome. Methods: In this paper, we introduce Virtual-Physio, a virtual assistant for remote rehabilitation integrated into a home-deployable low-cost physiotherapy monitoring system called 2Vita-B Physical. Virtual-Physio automatically (i) provides real-time online feedback to the patients while they perform the rehabilitation exercises, and (ii) evaluates a whole exercise session, so that a physiotherapist can focus only on cases that deserve more attention. Results: We experimented with Virtual-Physio on 51 individuals whose performances were also evaluated by an equipe of physiotherapists as a reference. The results (i) highlight good patient acceptability for the virtual assistant, and (ii) show that the proposed machine learning approach can effectively perform an automated evaluation of rehabilitative movements. Conclusions: We believe that the findings presented in this study provide a valid foundation for future improvements to home rehabilitation supported by the integration of motion capture and Machine Learning.