Self-powered smart skins for multimodal tactile perception based on triboelectric and hygroelectric working principles

Abstract

Human being perceives multiple tactile modalities in the process of sensation on the skin and interpretation in the brain. To date, several sensing techniques facilitate the accurate measurement of individual tactile modality, but multimodal static and dynamic sensing remain challenging. Moreover, low-cost and highly efficient interpretation techniques are still required for tactile perception. Herein, we present cost-effective and high-performing self-powered smart skins that mimic multimodal tactile perception, enabling accurate perception of pressure, vibration, and humidity in the process of sensation on the smart skin and interpretation by machine learning. The dynamic and static stimuli are encoded by triboelectric and hygroelectric principles in the smart skins, respectively, while the hygroscopic nature empowers humidity sensation capability in the smart skin with an accuracy rate as high as 84.0%−100.0%. We believe our smart skin will enable the smooth transition of e-skin into practical applications, such as robotics, prosthetics, healthcare, and intelligent industry.