Research on enhancing human-machine interaction in medical exoskeleton devices through the integration of artificial intelligence

This paper explores the integration of Artificial Intelligence (AI) to enhance human-machine interaction in medical exoskeleton devices. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics can significantly improve the efficiency and comfort of these devices. Machine learning algorithms analyze sensor data in real-time, optimizing control strategies to adapt to user movements. NLP enables intuitive control through voice commands, reducing the cognitive and physical burden on users. Predictive analytics anticipates user needs, enhancing responsiveness and reducing the risk of errors. Case studies of AI-integrated exoskeletons, like the EksoGT and ReWalk, demonstrate improved rehabilitation outcomes and user satisfaction. Despite challenges such as data privacy and the need for significant investment, the benefits of AI integration are substantial. This paper provides insights into the potential of AI to transform medical exoskeletons, offering new levels of independence and quality of life for individuals with mobility impairments. Future research should focus on developing advanced AI algorithms and exploring new applications to further enhance user experience and device performance.