AI‐Enabled Adaptive Eutectogel Skin for Effective Motion Monitoring with Low Signal Artifacts

Abstract Interfacial gel compliance is essential for the stable monitoring of physiological electrical signals. Current gel materials often fail to maintain stable operation at the skin interface, which is subject to constant change, due to an inadequate balance of viscoelastic properties. In this study, a dynamic adaptive gel network involving metal coordination with hierarchical hydrogen bonding is developed. The multilayered supramolecular structure has enabled the polymer chains to generate new physical entanglements upon dissociation. This dynamic cross‐linking allows the eutectogel to sustain stable viscosity and elasticity across a broad frequency range (10 −7 –340 Hz). Furthermore, the metal‐based eutectogel exhibits enhanced stretchability (1800%), good electrical conductivity (125 mS m −1 ), a wide operating temperature range (−70–100 °C), and strong interfacial adhesion. This adaptive eutectogel offers superior stability in the acquisition of electrical signals when compared with standard commercial gels. Viable application of the resultant strain sensors is demonstrated in human–machine interaction (HMI) and virtual reality (VR) haptic interaction. In addition, a convolutional neural network (CNN) algorithm is employed to develop an intelligent system for evaluating motion states using surface electromyography (sEMG) signals, achieving an accuracy of 94.1%.