• Advanced Fiber Materials
  • Vol. 7, Issue 5, 00565 (2025)
Weili Zhao1、4, Vuong Dinh Trung1、4, Fang Li2, Yinjia Zhang1、4, Haoyi Li3, Jun Natsuki4, Jing Tan3, Weimin Yang3, and Toshiaki Natsuki4、5
Author Affiliations
  • 1Graduate School of Medicine, Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda, Nagano 386-8567, Japan
  • 2International Joint Laboratory of New Textile Materials and Textiles of Henan Province, Zhongyuan University of Technology, Zhengzhou 450007, China
  • 3College of Mechanical and Electronic Engineering, Beijing University of Chemical Technology, Beijing 100029, China
  • 4Institute for Fiber Engineering (IFES), Interdisciplinary Cluster for Cutting Edge Research (ICCER), Shinshu University, 3-15-1, Tokida, Ueda, Nagano 386-8567, Japan
  • 5Faculty of Textile Science and Technology, Shinshu University, 3-15-1, Tokida, Ueda, Nagano 386-8567, Japan
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    DOI: 10.1007/s42765-025-00565-2 Cite this Article
    Weili Zhao, Vuong Dinh Trung, Fang Li, Yinjia Zhang, Haoyi Li, Jun Natsuki, Jing Tan, Weimin Yang, Toshiaki Natsuki. Hierarchical Synergistic Engineering for Machine Learning-Assisted Gesture Recognition and Integrated Thermal Management[J]. Advanced Fiber Materials, 2025, 7(5): 00565 Copy Citation Text show less

    Abstract

    Flexible strain sensors are revolutionizing human–machine interactions and next-generation health care by enabling real-time monitoring of human motion and precision medical treatment. However, developing lightweight flexible strain sensors that combine high sensitivity with a broad monitoring range remains a significant challenge. To address this challenge, an advanced structural engineering strategy based on the sodium chloride (NaCl) template sacrificial method is employed to simultaneously increase sensitivity and mechanical robustness. By leveraging a NaCl template sacrificial method, a hierarchical synergistic conductive network is constructed within the thermoplastic polyurethane (TPU) matrix formed via in situ growth. This design enables ultra-high sensitivity across a broad strain range, offering promising potential for wearable sensing applications. The resulting sensor exhibits exceptional performance characteristics, including a low detection limit (0.176%), high sensitivity (gage factor, GF = 331.7), wide sensing range (up to 230.1%), rapid response/recovery times (133 ms/133 ms), and remarkable durability exceeding 4000 cycles. Furthermore, the sensor demonstrated excellent electrothermal conversion performance with a positive temperature coefficient of 0.00207 °C-1 and an achievable saturation temperature of 54.2 °C (1.0 A). Finally, the sensor was successfully integrated into a smart wearable system, enabling precise recognition and classification of multiple gestures through machine learning algorithms while also exhibiting significant potential for inflammation hyperthermia therapy.
    Weili Zhao, Vuong Dinh Trung, Fang Li, Yinjia Zhang, Haoyi Li, Jun Natsuki, Jing Tan, Weimin Yang, Toshiaki Natsuki. Hierarchical Synergistic Engineering for Machine Learning-Assisted Gesture Recognition and Integrated Thermal Management[J]. Advanced Fiber Materials, 2025, 7(5): 00565
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