Adaptive AI in Neuro-Rehabilitation

This invention revolutionizes the use of Functional Electrical Stimulation (FES) systems in neuro-rehabilitation. Current FES systems rely on static models that lack real-time adaptability to patient progress and environmental changes, which limits their effectiveness. The core innovation lies in a device and method utilizing a pre-trained deep reinforcement learning (DRL) model to generate optimized control signals for FES systems. The DRL model, trained with extensive patient data and simulated musculoskeletal models, outputs personalized FES parameters that adapt dynamically during therapy sessions. This allows for safer and more efficient rehabilitation, enhancing motor recovery and patient comfort. The broader impact includes advancing neuro-rehabilitation, promoting brain plasticity, and improving lives of individuals with motor impairments, making the technology a crucial player in healthcare innovation.