Gesture Capture and Recognition of Wearable Systems Based on Smart Textile Materials

Wearable systems are gaining popularity across various domains, offering opportunities for innovative applications in human-robot interaction and healthcare. This paper explores the development of a wearable system utilizing smart textile materials for gesture capture and recognition. The device, constructed using core–shell-structured composite yarns, seamlessly integrates with the user’s wrist, enabling swift and accurate detection of wrist movements. By leveraging technologies such as Leap Motion for gesture capture and signal processing techniques including variational mode extraction and Hilbert transform, the device is capable of extracting features and classifying gestures. Experimental results demonstrate the effectiveness of the device in capturing and recognizing various wrist movements, the accuracy rate reaches 93.86%. The integration of wearable technology, signal processing, and machine learning provides effective avenues for enhancing human-robot interaction and advancing our understanding of human physiology and behavior.