Self-Assembly MXene/PDA@Cotton Fabric Pressure Sensor Integrated with Deep Learning for Sign Language Recognition

Abstract In recent years, smart textiles and flexible wearable products have garnered significant attention in fields such as human-computer interaction, medical rehabilitation training, and motion monitoring. Flexible pressure sensors have attracted significant attention due to their excellent flexibility, stability, and multifunctional integration. Herein, a multifunctional wearable MXene/polydopamine (PDA)@cotton fabric pressure sensor was developed by modifying weft-knitted cotton fabric based on a dual hydrogen bond self-assembly strategy. The MXene/PDA@cotton fabric pressure sensor demonstrates wide linear detection range (0–146 kPa), high sensitivity (0.95 kPa–1), fast response/recovery times (16.434 and 11.952 ms), and outstanding stability after over 5000 cyclic tests. This sensor can achieve the monitoring of physiological parameters for human state detection, such as facial expression signals, abdominal respiratory signals, and joint bending signals. Furthermore, by integrating the MXene/PDA sensors into the interphalangeal and metacarpophalangeal joints of a cotton glove, combined with intelligent algorithms and the human-computer interaction system, static gesture recognition and dynamic sign language translation were successfully realized based on the smart glove. This work demonstrates the potential application of flexible pressure sensors in intelligent human-computer interaction, providing new insights for developing next-generation sign language recognition systems.