Soft Pneumatic Exoskeleton Glove with Feedback Control for Precise Rehabilitation Training

Hand dysfunction is a prevalent issue that requires effective rehabilitation solutions. Traditional rehabilitation methods often face challenges due to the limited availability of therapists, leading to the growing adoption of robotic exoskeletons. Although rigid exoskeletons help, they are often heavy and uncomfortable, making soft robotic exoskeletons a preferred alternative. Soft pneumatic exoskeleton gloves offer flexibility, safety, and adaptability, making them ideal for stroke rehabilitation. However, current designs face challenges such as low precision in movement execution, limited adaptability, and lack of realtime sensor feedback. This research addresses these challenges by developing a soft pneumatic exoskeleton glove with PID feedback control for enhanced precision and adaptability. The system incorporates bellows-based actuators for natural hand movement, adaptive pressure control for personalized assistance, and multiple training modes to cater to various rehabilitation needs. A sensor-integrated control strategy ensures real-time motion tracking and precise actuation, improving rehabilitation effectiveness. Experimental results confirm that the proposed system provides smooth, controlled finger movements, improves user comfort, and enhances rehabilitation outcomes. Future advancements may integrate machine learning-based adaptive control for further optimization.