Design and Development of AI-Assisted Wearable Robotics for Healthcare Applications

The rapid advancement of wearable robotic technologies has opened new possibilities for improving healthcare delivery, rehabilitation, and patient mobility. This paper presents the design and development of an AI-assisted wearable robotic system intended to support healthcare applications such as physical rehabilitation, mobility assistance, and injury prevention. The proposed system integrates lightweight wearable hardware with intelligent control algorithms to adaptively assist human motion in real time. Machine learning models are employed to analyze multimodal sensor data, including inertial measurement units, electromyography signals, and force sensors, enabling accurate intention detection and personalized assistance levels. An adaptive control framework combining supervised learning and reinforcement learning is introduced to optimize motion support while ensuring user comfort and safety. Experimental evaluations conducted with healthy participants demonstrate improved motion stability, reduced muscular effort, and enhanced task performance compared to conventional non-intelligent wearable systems. The results highlight the potential of AI-assisted wearable robotics to deliver personalized, efficient, and scalable healthcare solutions, paving the way for next-generation assistive and rehabilitative technologies.