In-home physical therapy is one of the best options for many individuals and families thanks to its convenience and because it makes possible to receive professional care in the comfort of your own home. To enable this therapeutic approach, this paper proposes the employment of a smartphone-centric platform for in-home rehabilitation. The platform helps physicians to monitor the patients remotely so avoiding hospitalization therapies that can be stressful. In more detail, the work is focused on the Movement Recognition (MR) functionality of the aforementioned platform. It compares algorithms, which process the signal provided by the embedded accelerometer sensor of the smartphone, able to recognize if a patient had performed the movements requested by the physicians. The provided performance comparison of different MR techniques shows that Support Vector Machine-based approaches have very good accuracy (up to 99.3%), thus making the in-home physical therapy reliable.
['Igor Bisio', 'Alessandro Delfino', 'Fabio Lavagetto', 'Andrea Sciarrone']
Enabling smartphone-centric platforms for in-home rehabilitation: A comparison among movement recognition approaches