Multi-component collaborative design yields robust hydrogel sensors with superior environmental adaptability for machine learning-assisted gesture recognition.

Journal: Journal of colloid and interface science
Published Date: Sep 6, 2025

Abstract

Developing high-performance wearable flexible sensors that can adapt well to complex environments has become a hotspot. Herein, a polyvinyl alcohol based composite hydrogel sensor with high mechanical strength, desirable frost/swelling resistance, and highly sensitive sensing performance was proposed by a multi-component collaborative design strategy. Meanwhile, an intelligent gesture recognition system was established by combining machine learning algorithm. With the synergistic effect of aramid nanofibers and polyaniline, a composite skeleton coupled with a rigid network and a hydrogen bond network was constructed in the hydrogel, and its phase transition behavior was regulated by a mixed solvent system of glycerol/water. The composite hydrogel sensor exhibited excellent mechanical properties (tensile strength: 2.22 MPa, toughness: 3.58 MJ/m3), good environmental adaptability (low-temperature resistance of -30 °C, swelling rate < 15 % after 20 days), and good sensitivity (gauge factor: 1.41). Furthermore, high-precision recognition of different gestures (accuracy close to 100 %) could be achieved by collecting dynamic resistance signals and training a multi-layer perceptron model. Therefore, this work will realize the performance integration of functional hydrogel sensors as flexible wearable electronic devices, and provide innovative ideas for intelligent sensing in complex scenarios.

Authors

  • Kejie Chen
    Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China.
  • Xin An
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Tianlong He
    Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, College of Engineering, Zhejiang Normal University, Jinhua 321004, China.
  • Yihao Jiang
    Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, College of Engineering, Zhejiang Normal University, Jinhua 321004, China.
  • Jiahui Shen
    Tianjin Hospital of Tianjin University (Tianjin Hospital), Tianjin, 300211, People's Republic of China.
  • Zhaochun Li
    College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
  • Xiaofan Ma
    College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou 311300, China.
  • Qingfeng Sun
    Infectious Disease Department, Ruian People's Hospital, Zhejiang, 325200, China.
  • Jiajia Zheng
    Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing, 100190, P. R. China.
  • Yiming Chen
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.

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