文献类型：期刊文献

英文题名：Smart self-powered insoles for real-time fall risk detection

作者：Zhang, Yihan[1];Yan, Yizhen[1];Wallis, Nathan[1];Sheng, Weihua[1];Zhu, Benson[1,2];Zhang, Zhang[3];Chen, Qiao[4];Zhang, Fan[1]

机构：[1]Univ Sussex, Sch Engn & Informat, Dept Engn & Design, Brighton BN1 9RH, England;[2]Zhejiang Gongshang Univ, Sussex Artificial Intelligence Inst, Hangzhou 310018, Peoples R China;[3]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China;[4]Univ Sussex, Sch Life Sci, Brighton BN1 9RH, England

年份：2026

卷号：530

外文期刊名：CHEMICAL ENGINEERING JOURNAL

收录：;EI(收录号：20260419969943);WOS:【SCI-EXPANDED(收录号:WOS:001678648500001)】；

基金：Acknowledgement Dr. Fan Zhang acknowledges the financial support from the Engineering and Physical Sciences Research Council (EPSRC) under the grant reference EP/Y022009/1, and The Royal Society RGS\R2\212222, IEC \NSFC\211303.

语种：英文

外文关键词：Smart insoles; Triboelectric nanogenerator; Fall risk detection; Multiple working modes

摘要：To reduce fall risks in vulnerable groups, such as the elderly and high-altitude workers, we have developed a self-powered, fall warning insole utilizing a high-sensitivity, fast-response flexible triboelectric nanogenerator (HSFR-TENG). Through structural optimization, the HSFR-TENG exhibits high stability, durability, sensitivity, and signal-to-noise ratio. The smart insole features a continuously covered hexagonal sensor array that accurately monitors changes in the user's center of gravity. A wired signal acquisition mode was used to establish detection rules for abnormal pressure distributions and fall risks. By integrating energy harvesting and sensing capabilities, the insole supports a completely wireless fall warning system, eliminating the need for external power or auxiliary signal processing devices. The system demonstrated a fall-risk detection accuracy exceeding 87% under controlled experimental conditions. Additionally, a dedicated smartphone application was developed to enhance user interaction with the smart insole, providing a convenient and user-friendly platform for monitoring and early warnings. This work presents a novel, fully autonomous, high-performance approach to wearable safety technology, improving the safety and quality of life for vulnerable and high-risk populations.