Evaluating neurorehabilitation exercises captured with commodity sensors and machine-learning framework

During the last decades, disease-related disabilities, primarily caused by stroke have increased worldwide. Neurorehabilitation exercise therapy plays a vital role in the recovery of such disabilities. However, due to global demographic changes and the increasing number of stroke patients, therapists are facing difficulties in coping with the demand. Consequently, the necessity for appropriate technical support to help the therapists for providing helpful progress feedback to the patients is becoming evident. So far, such technological systems are not yet available for clinical usage. Moreover, there is still a lack of research demonstrating the possibility of pursuing the therapeutic exercises by the patients themselves at their homes using non-invasive commodity sensors. In this work, we design a system pipeline containing commodity cameras by which the patients would be able to record their exercises at home; we also evaluate and analyze the acquired data using an off-the-shelf machine-learning framework. The medical experts can utilize our system to monitor the patients' progress over the prescribed duration of the therapy. Here, rather than using specialized sensors with the body to acquire the movement information of the body joints, which some of the existing works use, we use a machine-learning framework to acquire this information. Our evaluation process demonstrates the situations in which these activities can be reliably acquired with commodity RGB cameras; moreover, the challenging aspects of the acquisition which can affect the accuracy of recognition of the framework are discussed and analyzed.