A Highly Sensitive, Conductive, and Flexible Hydrogel Sponge as a Discriminable Multimodal Sensor for Deep‐Learning‐Assisted Gesture Language Recognition

Abstract

Flexible multimodal sensors have gained increasing popularity for applications in healthcare and extreme environment operations owing to their all‐around environmental perception and data acquisition capabilities. However, fabricating a magnetism‐mechanics‐humidity multimodal sensor that possesses high sensitivity without signal overlapping while in a facile methodology remains a great challenge. Herein, a highly sensitive, conductive, and flexible hydrogel sponge sensor with discriminable magnetism, mechanics, and humidity sensing capability is proposed, which shows stable pore size (19.30 µm) and satisfactory mechanical properties based on the synergistic hydrogen bonding among sodium alginate, poly(vinyl alcohol) and glycerol. The proposed sensors can not only display favorable humidity sensing ability with rapid response/recovery time (2.5/4 s) but also possess enhanced sensitivities (a gauge factor of 0.46 T−1 for magnetic field, −1.16 kPa−1 for pressure), superior stability and durability (over 8000 cycles). Benefiting from the separated capacitive and resistive response signals, the sensors can precisely distinguish the magnetic, mechanical, and humidity stimuli without cross‐talk. Further, the sensor arrays assisted by the deep learning algorithm are developed to realize gesture language recognition with a high accuracy of 99.17%. It can be believed that this high‐performance sensor will have good prospects in future soft electronics and human‐machine interaction systems.