When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists’ evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error, showing that the proposed system can be trained to evaluate the movement quality of the entire exercise movement using as little as the initial 5s of the exercise performance. The proposed platform may help ensure high quality exercise performance and provide virtual feedback of experienced therapists during at-home rehabilitation.
Stroke is a leading cause of death and disabilities in adults, and the majority of its survivors suffer from upper extremity paresis . There is scientific evidence that repetitive rehabilitation exercises and training could improve motor abilities as a result of motor learning processes . Among many, a reaching movement is a fundamental component of daily movement that requires the coordination of multiple upper extremity segments . It is shown that repetitive reaching exercises improve the smoothness, precision, and speed of arm movements . To continue to improve and to sustain motor function, it is clinically important that patients continue to engage in rehabilitation exercises even outside the clinical settings , which emphasizes the importance of the home-based therapy.
via A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study – IEEE Conference Publication