Modeling rehabilitation dataset to implement effective AI assistive systems

Abstract The demand for automated systems monitoring and supporting patients in their home-based recovery programs is substantial. While emerging technologies have been proposed as potential solutions to enhance at-home patient care, limited systems are in place due to their challenges in offering real-time monitoring and corrective feedback. Most proposed methodologies provide an overall measure or score for the executed movement. The proposed work involves the adaptation of an existing published dataset to cater to the needs of a system capable of remotely assisting patients, effectively acting as a virtual physical therapist able to provide corrective feedback. A dataset containing a set of three simple exercises for shoulder rehabilitation was processed. Each movement was meticulously annotated for temporal and categorical motion domains to monitor exercise execution in terms of the range of motion completeness and to evaluate compensatory movement patterns. This work carries substantial significance by offering a standardized and easily accessible model for human movement data, thus fostering the advancement of digital assistive systems designed to support home-based rehabilitation programs.