Deep‐Learning‐Assisted Thermogalvanic Hydrogel E‐Skin for Self‐Powered Signature Recognition and Biometric Authentication

Abstract

Self‐powered electronic skins (e‐skins), as on‐skin human‐machine interfaces, play a significant role in cyber security and personal electronics. However, current self‐powered e‐skins are primarily constrained by complex fabricating process, intrinsic stiffness, signal distortion under deformation, and inadequate comprehensive performance, thereby hindering their practical applications. Herein, a novel highly stretchable (534.5%), ionic conductive (4.54 S m−1), thermogalvanic (1.82 mV K−1) hydrogel (TGH) is facilely fabricated by a one‐pot method. Owing to the formation of Li+(H2O)n hydration structure, the TGH presents excellent anti‐freezing and non‐drying performance. It remains flexible and conductive (3.86 S m−1) at −20 °C and shows no obvious degradation in the thermoelectrical performance over 10 days. Besides, acting as a self‐powered e‐skin, the TGH combined with deep learning technology for signature recognition and biometric authentication is successfully demonstrated, achieving an accuracy of 92.97%. This work exhibits the TGH‐based e‐skin's tremendous potential in the new generation of human‐computer interaction and information security.

Thermogalvanic organohydrogel-based non-contact self-powered electronics for advancing smart agriculture

“…We also determined the thermal conductivity of the organohydrogel using the steady-state method. 35,36 The thermal conductivity in Fig. 2h and Fig.…”

Latest developments and trends in electronic skin devices

“…ML methods can greatly enhance the intelligence level of an e-skin system, which can greatly improve the performance of human-machine interfaces (HMI), and show a broad application prospect in medical health, rehabilitation therapy and remote monitoring [201][202][203][204] . They can learn feature signals corresponding to a certain stimulus from a large amount of experimental data, which can recognize different types of stimuli (such as gesture, touch strength, texture, and shape) [205][206][207][208] .…”

Strain-Temperature Dual Sensor Based on Deep Learning Strategy for Human–Computer Interaction Systems

“…Traditional conductive materials such as metals and organic semiconductors have limitations in flexible electronics due to issues such as a lack of elasticity and biocompatibility and low Seebeck coefficients. , As a result, hydrogels are emerging as a promising material platform for flexible electronics, offering excellent properties, including flexibility, stretchability, and the ability to detect environmental stimuli such as pressure, strain, and temperature simultaneously, and the advantages of action monitoring under complex conditions through deep learning and super hydrophobicity. − For example, inspired by the flexible tube feet of starfish, Liu et al proposed a flexible gesture recognition glove (GRG), and the fabricated flexible sensor provided a wide working range, reliable repeatability, and low detection limits. By combining high-performance MPTS with machine learning algorithms, the proposed GRG system achieved intelligent recognition of 16 underwater gestures …”