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Edge-Driven Digital-Twin Framework for Real-Time, Privacy-Preserving Adaptive Robotic Tele-Rehabilitation in Home IoMT Environments
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Author(s): T. Grace Shalini (S.R.M. Institute of Science and Technology, India), P. Bhavesh (S.R.M. Institute of Science and Technology, India), S. S. Krishikaa Mathi Bharathi (S.R.M. Institute of Science and Technology, India)and Nayantra Ramakrishnan (S.R.M. Institute of Science and Technology, India)
Copyright: 2026
Pages: 34
Source title:
Robotics and IoT Synergy in Next-Generation Healthcare
Source Author(s)/Editor(s): Safaa Najah Saud Al-Humairi (Management and Science University, Malaysia), Prasitthichai Naronglerdrit (Kasetsart University, Thailand), Nattapon Chantarapanich (Kasetsart University, Thailand)and Sujin Wanchat (Kasetsart University, Thailand)
DOI: 10.4018/979-8-3373-5447-7.ch005
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Abstract
As populations age and remote care demand grows, tele-rehabilitation must evolve beyond rigid, cloud-heavy models. We propose an edge-driven digital twin framework integrating wearable robotic exoskeletons, home IoMT sensors, and edge compute nodes to create real-time, privacy-preserving rehabilitation. Patient-specific musculoskeletal twins are continuously updated via multimodal sensor fusion, while adaptive algorithms—optimized through federated learning—personalize therapy without transmitting raw data. A lightweight middleware ensures sub-100 ms response, GDPR/HIPAA compliance, and seamless integration with clinician dashboards. In a pilot study on post-stroke gait rehabilitation, the framework improved recovery speed by up to 30% and reduced data exposure risk by 80%, highlighting its potential to transform intelligent, patient-centric home care.
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