Artificial Intelligence-driven Remote Monitoring Model for Physical Rehabilitation

Abstract

In recent times, the quality of life of several individuals has been affected by chronic diseases. Traditional forms of rehabilitation occasionally involve face-to-face sessions, which restricts accessibility and presents challenges for real-time monitoring. Lack of comprehensive understanding of the aspects impacts long-term patient engagement and adherence to remote rehabilitation programs. Individuals and healthcare systems incur a significant portion of the costs associated with rehabilitation treatment. A home-based rehabilitation program reduces the rehabilitation cost. However, the clinicians’ absence may affect the effectiveness of rehabilitation programs. There is a demand for an artificial intelligence-based remote monitoring model for evaluating the physical movements of individuals. Therefore, the study proposes a framework for generating scores for physical rehabilitation exercises. It supports the home-based rehabilitation program by assessing the individual’s movements. The authors employ the You Only Look Once V5–ShuffleNet V2-based image processor for generating scores using the variations between the joints. In addition, they build bidirectional long short-term memory networks for delivering a score for each exercise. Finally, the two outcomes are compared using the modulated rank averaging method for presenting the final score. The authors evaluate the performance of the proposed model using the KiMoRe dataset. The comparative analysis outcome suggested that the proposed model obtained an exceptional mean absolute deviation, mean absolute percentage error, and root mean square error of 0.425, 1.120, and 0.985, respectively. It highlighted the significance of the proposed framework in assessing the individual’s physical movement. Further studies will improve the performance of the proposed framework.