Kirigami Cube‐Inspired Triboelectric Vector Sensor for Human–Machine Interaction

Abstract With the rapid development of artificial intelligence, sensors play a vital role as a crucial medium for human–machine interaction (HMI). However, due to the point‐to‐point connections of rigid sensors, transmitting signals in multiple orientations requires a large number of wirings. To address this challenge, a self‐powered triboelectric vector sensor based on a kirigami cube structure is proposed. This sensor enables the conversion of forces from multiple directions into electrical signals using only a single output channel. The kirigami cube structure with a negative Poisson's ratio characteristic enables self‐recovery of the sensor structure. Through optimization of the structure and materials, the sensor achieves a response time of 48 ms and a minimum resolution of 0.2 N, demonstrating excellent self‐recovery and sensing capabilities. Furthermore, by integrating machine learning algorithms for real‐time decoding of the single‐channel signal, precise recognition of multiple directional movements is achieved (accuracy of 99.38%). The gradient‐based recognition approach enables real‐time and accurate identification of electrical signals while effectively suppressing the interference caused by baseline drift. This sensor is demonstrated in two HMI applications, including a smart joystick and fingertip tactile sensing, showcasing its potential in HMI.