DeepPhysio
Gianmarco Sanesi, Andrew D. Bagdanov, Marco Bertini, Alberto Del Bimbo · 2019
This paper describes an action classification pipeline for detecting and evaluating correct execution of actions in video recorded by smartphone cameras; the use case is that of simplifying monitoring of how physiotherapeutic exercises are performed by patients in the comfort of their own home, reducing the need of physical presence of therapists. Our approach is based on applying DensePose to every frame of acquired video and subsequent sequence analysis by an LSTM network. We validate our proposed recognition approach on a subset of the NTU RGB+D dataset in order to determine the best classification pipeline for this application. We also describe a mobile, cross-platform application called DeepPhysio that is designed to allow at physiotherapy patients to obtain immediate feedback about the correctness of the physical exercises. Preliminary usability analysis shows that this type of application can be effective at monitoring physiotherapy exercises.