An analytical framework for image-based automatic evaluation and intervention in motor function rehabilitation †

Many healthcare systems are facing a severe shortage of rehabilitation resources. In this paper, an analytical framework for image-based automatic evaluation and intervention in motor function rehabilitation is introduced. The framework uses a regular camera to capture patient movements, adopts machine learning methods to evaluate scale results representing the status of a patient, considers the impacts of different clothing, and finally, generates personalized intervention plan. Such a framework overcomes the time-consuming limitation of traditional rehabilitation evaluation that requires extensive sessions and physician’s presence. Moreover, the intervention plans generated by the framework demonstrate consistency with those developed by physicians. By integrating evaluation and intervention, the proposed method can serve as a quantitative tool to reduce the burden of physicians, improve system efficiency and patient outcomes in rehabilitation processes, and help satisfy the growing need for accessible and efficient rehabilitation services.