Adopting Machine Learning-Based Pose Estimation as Digital Biomarker in Motor Tele-Rehabilitation

Nowadays, tele-rehabilitation has emerged as an effective approach for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering the reintegration of patients into society, also pushing down clinical costs. Cloud computing in combination with Edge Computing and Artificial Intelligence (AI) are the main enablers for tele-rehabilitation. In particular, Edge rehabilitation devices can act as smart digital biomarkers sending quantifiable physiological and behavioural patients' data to the Hospital Cloud. However, due to hardware limitations, it is not clear which Machine Learning (ML) models can be executed in cheap Edge devices. In this paper, we aim at answering this question. In particular, several ML-based pose estimation models (i.e., PoseNet, MoveNet and BlazePose) have been tested and assessed on the Edge, identifying the best one and demonstrating the feasibility of such an approach.