Highly Sensitive and Mechanically Stable MXene Textile Sensors for Adaptive Smart Data Glove Embedded with Near-Sensor Edge Intelligence

Smart data gloves capable of monitoring finger activities and inferring hand gestures are of significance to human–machine interfaces, robotics, healthcare, and Metaverse. Yet, most current smart data gloves present unstable mechanical contacts, limited sensitivity, as well as offline training and updating of machine learning models, leading to uncomfortable wear and suboptimal performance during practical applications. Herein, highly sensitive and mechanically stable textile sensors are developed through the construction of loose MXene-modified textile interface structures and a thermal transfer printing method with the melting-infiltration-solidification adhesion procedure. Then, a smart data glove with adaptive gesture recognition is reported, based on the integration of 10-channel MXene textile bending sensors and a near-sensor adaptive machine learning model. The near-sensor adaptive machine learning model achieves a 99.5