Decoupled Temperature–Pressure Sensing System for Deep Learning Assisted Human–Machine Interaction

Abstract

AbstractWith the rapid development of intelligent wearable technology, multimodal tactile sensors capable of data acquisition, decoupling of intermixed signals, and information processing have attracted increasing attention. Herein, a decoupled temperature–pressure dual‐mode sensor is developed based on single‐walled carbon nanotubes (SWCNT) and poly(3,4‐ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) decorated porous melamine foam (MF), integrating with a deep learning algorithm to obtain a multimodal input terminal. Importantly, the synergistic effect of PEDOT:PSS and SWCNT facilitates the sensor with ideal decoupling capability and sensitivity toward both temperature (38.2 µV K−1) and pressure (10.8% kPa−1) based on the thermoelectric and piezoresistive effects, respectively. Besides, the low thermal conductivity and excellent compressibility of MF also endow it with the merits of a low‐temperature detection limit (0.03 K), fast pressure response (120 ms), and long‐term stability. Benefiting from the outstanding sensing characteristics, the assembled sensor array showcases good capacity for identifying spatial distribution of temperature and pressure signals. With the assistance of a deep learning algorithm, it displays high recognition accuracy of 99% and 98% corresponding to “touch” and “press” actions, respectively, and realizes the encrypted transmission of information and accurate identification of random input sequences, providing a promising strategy for the design of high‐accuracy multimodal sensing platform in human–machine interaction.