Integrating wearable health devices with AI and edge computing for personalized rehabilitation

Wearable health devices have revolutionized rehabilitation by enabling real-time monitoring of physiological parameters, fostering early disease detection, and promoting proactive health management especially for young students. The integration of artificial intelligence (AI) and edge computing with these devices has further enhanced their capabilities by enabling advanced data processing, predictive analytics, and personalized healthcare recommendations. This review explores the latest advancements in AI-driven and edge-enabled wearable health technologies, emphasizing their applications in chronic disease management, real-time patient monitoring, and personalized healthcare solutions. Additionally, it discusses the key AI and edge techniques, including machine learning, deep learning, and federated learning, that enhance the effectiveness of wearable devices. Despite their immense potential, challenges such as data privacy, interoperability, and ethical concerns remain significant barriers to widespread adoption. This paper provides a comprehensive overview of current solutions and emerging trends in AI-integrated and edge-enabled wearable health systems, offering insights into future research directions and technological advancements that will shape the next generation of personalized healthcare solutions.