Physical Integrated Digital twin-based Interaction Mechanism of Artificial Intelligence Rehabilitation Robots Combining Visual Cognition and Motion Control

Within the evolving landscape of rehabilitation robotics, integrating cyber-physical systems with digital twin generation presents a singular paradigm for enhancing the effectiveness and personalisation in interaction applications. This paper introduces an interaction mechanism for artificial intelligence (AI) rehabilitation robots that synergises visible cognition and movement management within a Cyber-physical device framework underpinned through a digital twin-based approach. Our method leverages the twin skills of AI to interpret complex visual stimuli and execute specific, adaptive movement control, thereby permitting a more intuitive and responsive rehabilitation system designed to individual patient wishes. The core of our proposed mechanism lies in developing a virtual twin for each affected person, a digital duplicate that dynamically updates and evolves in response to the affected person’s progress and interactions with the AI rehabilitation robotic. This virtual dual serves as a sandbox for simulating and optimising rehabilitation sporting events, permitting therapists to expect results, personalise remedy plans, and make knowledgeable adjustments in actual time. We integrate superior visual cognition algorithms to interpret the affected person’s actions, facial expressions, and gestures, facilitating a nuanced knowledge of the patient’s attempt, discomfort, or engagement tiers. Our experimental consequences display significant improvements in patient outcomes, such as better motor function, increased engagement, and quicker recuperation times. The virtual dual-based totally interplay mechanism no longer best bridges the space among virtual and physical geographical regions; however, it also allows continuous learning and version of the AI rehabilitation robots, promising a brand-new generation of personalised, green, and patient-centric rehabilitation practices.