Bioinspired Monopolar Controlled Ionic Hydrogels for Flexible Non-Contact Human–Machine Interfaces

Abstract

Most flexible human–machine interfaces emulate the tactile system of the skin, which has the risk of contact damage. Additionally, contact deformation often leads to a hysteresis response. Non-contact interaction can address these problems. Inspired by the electroreception capabilities of the elephantnose fish, this study introduces a non-contact sensing model employing monopolar controlled ionic hydrogel. Compared to most existing mutual capacitive non-contact sensing models, this model not only boosts responsivity by over 3.5 times but also streamlines the sensing architecture. Utilizing this sensing model, a flexible non-contact human–machine interface is developed by organizing three differently shaped hydrogels into an asymmetric configuration. This device reliably discerns six non-contact gestures using machine learning algorithms and supports at least eleven interactive functions by detecting the duration of gestures, enabling continuous real-time control over external devices. This advancement heralds a more liberated paradigm of human–machine interaction with promising implications for the Internet of Things.