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

• Published in: Advanced functional materials 2024-07, Vol.34 (30), p.n/a
• Main Authors: Li, Yanpeng, Matsumura, Guren, Xuan, Yan, Honda, Satoko, Takei, Kuniharu
• Format: Article
• Language: English
• Subjects: action recognitions; e‐skin; Graphene; Liquid alloys; Liquid metals; Machine learning; machine learnings; Moving object recognition; Pressure sensors; Sensor arrays; stretchable devices; Stretching
• ISSN: 1616-301X; 1616-3028
• DOI: 10.1002/adfm.202313824

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.