Design and Gesture Recognition of Wrist Joint Posture Sensor Based on Fiber Bragg Gratings

The wrist joint is the main part of hand movement control, and its posture detection is very important for gesture recognition. This study presents a novel and straightforward method for detecting wrist joint posture utilizing fiber Bragg grating (FBG) sensors. FBGs integrated with a composite substrate of polydimethylsiloxane (PDMS) and polyimide (PI) were affixed to the skin surface of specific forearm muscles to monitor forearm muscle deformation induced by wrist joint movement. The response characteristics and applicability of the designed sensor were validated through simulation and testing. A long short-term memory (LSTM) algorithm with an attention mechanism was employed for training and predicting eight static gestures from testers with varying physiques. Experimental results demonstrated that in comparison to other substrates, FBG with PDMS and PI composite substrates exhibited superior anti-creep properties, fast response and recovery times (198/202 ms), high sensitivity (7.09 pm/°), and good repeatability (standard deviation (SD) of 2.48%). The LSTM model could effectively predict the eight static gestures of the testers, with 99.48% recognition accuracy for the same person and 96.67% recognition accuracy for ten different persons. The proposed sensors would have potential applications in the field of rehabilitation training, virtual reality (VR) or augmented reality (AR), human-computer interaction, and so on.