Robust and anti-swelling MXene composite hydrogel sensors for intelligent gait monitoring via a CNN-LSTM network

Abstract

Although flexible hydrogel sensors hold great promise in health monitoring, their reliability in long-term wearable scenarios is severely compromised by intrinsic hydrophilicity-induced uncontrollable swelling and signal drift. To address this critical bottleneck, we developed an MXene-poly(acrylic acid) (PAAc)-gelatin (MPG) composite hydrogel featuring superior anti-swelling properties. By incorporating Zr⁴⁺ coordination crosslinking, a robust “covalent-physical-coordination” triple network was constructed. This design not only endows the hydrogel with excellent mechanical toughness but also significantly restricts water uptake, achieving an ultralow 30-day cumulative swelling degree of 1.74%, thereby ensuring structural stability in humid environments. Based on this advanced material, a smart insole sensing system was fabricated. Benefiting from the high signal-to-noise-ratio and temporal stability of the MPG sensors, the proposed CNN-LSTM hybrid deep-learning model effectively extracts spatiotemporal features from complex gait signals. The system achieves a gait phase recognition accuracy of 88.0%, significantly outperforming conventional machine learning algorithms. This work demonstrates that the integration of high-performance anti-swelling materials with advanced deep learning algorithms represents an effective strategy for achieving next-generation precision wearable diagnostics.