Design of a Parkinson’s Rehabilitation Management System Based on Multimodal Learning and Digital Twin Technology

Parkinson’s disease (PD), a prevalent neurodegenerative disorder, significantly impairs motor functions, necessitating effective rehabilitation management. This paper presents the design of a Parkinson’s rehabilitation management system leveraging multimodal learning and digital twin technology. By integrating data from diverse sources—such as speech signals, hand motion data, and gait videos—this system comprehensively captures PD symptoms, facilitating early detection and accurate diagnosis. Employing deep learning techniques, the system analyzes and fuses multimodal data, enhancing diagnostic precision and providing personalized rehabilitation plans. The digital twin component creates a virtual model of the patient, continuously monitoring motor functions, medication responses, and symptom changes, thus optimizing treatment plans and improving quality of life. Case studies demonstrate the system’s efficacy in speech recognition, tremor identification, and tremor severity assessment, highlighting its role in enhancing patient self-management and rehabilitation outcomes. The proposed system not only advances PD rehabilitation management, but also sets a precedent for intelligent healthcare solutions by integrating multimodal data analysis with digital twin technology.