Coupling thermogalvanic and piezoresistive effects in a robust hydrogel for Deep-Learning-Assisted Self-Powered sign language and object recognition

The human hand provides rich sensory information for proper interaction with the environment. However, designing passive hand-sensing arrays with robust mechanical properties, high conductivity, ease of integration and reliability remains a long-term challenge. Here, we develop a robust dynamic crystalline ion-injected thermoelectric gel (DCI-TEG) via a simple solvent-exchange salt-out strategy. The resultant DCI-TEG exhibits high tensile strength (3.8 MPa), toughness (7.5 MJ m−3), and ionic conductivity of 1.84 S m−1. Benefiting from non-covalent crosslinking, the gel electrolyte and electrodes can be fully integrated into modules with tight physical and electrical contact, facilitating a stable and reliable signal output. By coupling thermogalvanic and piezoresistive effects, the DCI-TEG permits self-powered timely sensing of pressure using current signals. By implanting ionic thermoelectric module arrays on a glove, a wearable pressure sensing route is demonstrated with the merits of self-power, low cost and simple preparation. With the help of deep learning algorithms, a self-powered recognition for sign language and objects is realized in wearable/portable scenarios by active high-accuracy pressure detection. This work gives people a new direction toward barrier-free passive communication and human–machine interaction in the next-generation artificial intelligence.