Ultra-strong Ionic Hydrogels for Machine Learning Enabled Human Machine Interfaces

Abstract

The next generation of human–machine interfaces demands materials that can conform to skin, sustain large deformations, and transduce signals with both robustness and intelligence. Conventional hydrogels rarely satisfy all these requirements, being mechanically fragile or restricted to passive sensing. Here we present a bioinspired ionic hydrogel platform that integrates mechanical resilience, biomimetic design, and machine learning recognition, transforming soft matter into an intelligent interface. The hydrogel is engineered as a hierarchical double network of poly(vinyl alcohol) (PVA) crystallites and cellulose nanofiber scaffolds, dynamically bridged by tannic acid–ion complexes and hydrated with glycerol. This architecture provides a tensile strength of 1.46 MPa at 481% elongation and a compressive modulus of 17.1 MPa. The hydrogel exhibits rapid response and recovery times of 74 and 55 ms under 30 kPa pressure and maintains stable relative resistance signals through 1000 compression cycles. Its intrinsic adhesion ensures conformal integration with skin for reliable monitoring of joint bending, posture, and subtle gestures, while biomimetic hydrogel ropes emulate muscle fibers to dissipate energy and maintain fatigue-resistant performance during large-amplitude motion. Beyond physical sensing, coupling the hydrogel with a pressure-sensing keyboard and a lightweight one-dimensional convolutional neural network enables keystroke dynamics to be classified with 95.2% accuracy, robust across 30–70% relative humidity and 16–32 °C. These results establish a multifunctional hydrogel platform that combines toughness, adhesion, and signal intelligence, paving the way for wearable sensors, posture monitoring, and secure digital interfaces.

Graphical Abstract

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