Stretchable Electronic Skin using Laser-Induced Graphene and Liquid Metal with an Action Recognition System Powered by Machine Learning

Monitoring tactile pressure and recognizing action are important functionalities for artificial electronic skin (e-skin). Furthermore, in order to create conformable coverings for 3D objects, an e-skin needs to be stretchable, without sacrificing sensitivity to tactile pressure. However, stretching of sensors normally affects their output stability. In this study, a stretchable e-skin is developed using laser-induced graphene and a liquid metal alloy, GaInSn, in an elastic ecoflex polymer to create a stretchable, resistive-type tactile pressure sensor. Furthermore, a pressure sensor array is fabricated as an e-skin, and output is signal-processed using machine learning. With this system, the e-skin also monitors its stretching state, with the result that tactile pressure can be calculated regardless of the degree of stretching. With machine learning-assisted e-skin, actions such as patting, sliding, and grabbing are successfully recognized in the manner of human skin. A stretchable tactile pressure sensor array with highly accurate tactile pressure detection is developed in both unstretched and stretched states as an electronic skin (e-skin). To achieve greater functionality, resembling that of human skin, action recognition over the e-skin is successfully demonstrated by analyzing pressure distribution and motions using an echo-state network regardless of the degree of stretching. image