Autonomous Exoskeletons for Real-Time Gait Analysis and Rehabilitation with AI for Mobility-Impaired Patients

摘要

This work discusses the design and implementation of autonomous exoskeletons that use artificial intelligence (AI) for real-time gait analysis and rehabilitation assistance for individuals with mobility impairments. The exoskeleton uses a Recurrent Neural Network (RNN) algorithm to analyze complex gait patterns and changes during ambulation. The technology provides quick feedback and adjusts according to patient requirements, facilitating personalized rehabilitation programs. The exoskeleton utilizes wearable sensors and machine learning methods to record and analyze kinematic data, allowing accurate monitoring of gait dynamics and identifying problems. This proactive strategy facilitates patient mobility restoration and enables healthcare personnel to assess progress efficiently. Integrating modern robotics and AI establishes autonomous exoskeletons as revolutionary instruments in rehabilitation treatment, providing a novel approach to promote mobility and improve the quality of life for those with gait deficiencies. Future efforts will focus on enhancing the AI algorithms and expanding the scope of applications.