Strain-Temperature Dual Sensor Based on Deep Learning Strategy for Human–Computer Interaction Systems
Xiaolong Wu, Xiaoyu Yang, Peng Wang, Zinan Wang, Xiaolong Fan, Wei Duan, Ying Yue, Jun Xie, Yunpeng Liu
Abstract
Thermoelectric (TE) hydrogels, mimicking human skin, possessing temperature and strain sensing capabilities, are well-suited for human–machine interaction interfaces and wearable devices. In this study, a TE hydrogel with high toughness and temperature responsiveness was created using the Hofmeister effect and TE current effect, achieved through the cross-linking of PVA/PAA/carboxymethyl cellulose triple networks. The Hofmeister effect, facilitated by Na + and SO 4 2– ions coordination, notably increased the hydrogel’s tensile strength (800 kPa). Introduction of Fe 2+ /Fe 3+ as redox pairs conferred a high Seebeck coefficient (2.3 mV K –1 ), thereby enhancing temperature responsiveness. Using this dual-responsive sensor, successful demonstration of a feedback mechanism combining deep learning with a robotic hand was accomplished (with a recognition accuracy of 95.30%), alongside temperature warnings at various levels. It is expected to replace manual work through the control of the manipulator in some high-temperature and high-risk scenarios, thereby improving the safety factor, underscoring the vast potential of TE hydrogel sensors in motion monitoring and human–machine interaction applications.