Optical Fiber Sensing-Integrated Soft Robotic Rehabilitation Glove for Multimodal Gesture Recognition and Intention-Driven Assistance

Soft pneumatic actuators provide compliant and comfortable hand rehabilitation, but conventional systems suffer from limited self-sensing capabilities and insufficient human–robot interaction (HRI). We present a segmented soft rehabilitation glove with a serpentine-embedded fiber Bragg grating (FBG) sensing structure, designed to alleviate bending-induced stress concentration and provide accurate, long-term joint-angle tracking. We further propose a lightweight temporal multimodal fusion network (LTMFNet) that integrates FBG and surface electromyography (sEMG) signals. The network employs an adaptive time–frequency channelwise self-attention (ATFCA) to capture discriminative temporal, spectral, and channel patterns in sEMG, while a convolutional minimal gated recurrent unit (minGRU) efficiently models long-range spatiotemporal dependencies in FBG sequences. Experimental results demonstrate that the serpentine FBG configuration reduces stress concentration by 8.58% and yields higher tracking accuracy compared to linear layouts. On a 20-class gesture recognition task, the model achieves an accuracy of 97.89% (±0.36%), enabling real-time, intention-driven rehabilitation.