A research framework for personalized rehabilitation system based on human-machine fusion digital twin

Digital twin (DT) technology demonstrates significant potential in personalized rehabilitation, yet existing approaches lack a systematic methodology for constructing adaptive and computationally efficient human-machine-integrated DT. This paper proposes a research framework for personalized rehabilitation systems based on human-machine integrated DT, featuring three key technical contributions: (1) A systematic approach for constructing human-machine fusion DT models, integrating individual-specific musculoskeletal modeling (based on MRI parameters and Hill-type dynamics) with multi-domain device modeling. Coupled differential-algebraic equations achieve rigorous mathematical representation of human-machine interactions; (2) Dynamic tracking strategy: Achieves continuous model adaptation through state monitoring, performance decay modeling, and validation/correction mechanisms; (3) Efficient solution scheme: Combines Krylov subspace-based model order reduction with cloud-edge collaborative computing architecture to enable real-time simulation. Rather than presenting a fully validated rehabilitation system, this work establishes a systematic methodological framework that integrates human-machine fusion modeling, coordinated dynamic updating, and efficient computational strategies for personalized rehabilitation. The proposed framework provides a theoretical foundation and technical roadmap for future proof-of-concept simulation studies, hardware implementation, and clinical validation.