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.