Nerve-Inspired Optical Waveguide Stretchable Sensor Fusing Wireless Transmission and AI Enabling Smart Tele-Healthcare.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID: 39630936

Abstract

Flexible strain monitoring of hand and joint muscle movement is recognized as an effective method for the diagnosis and rehabilitation of neurological diseases such as stroke and Parkinson's disease. However, balancing high sensitivity and large strain, improving wearing comfort, and solving the separation of diagnosis and treatment are important challenges for further building tele-healthcare systems. Herein, a hydrogel-based optical waveguide stretchable (HOWS) sensor is proposed in this paper. A double network structure is adopted to allow the HOWS sensor to exhibit high stretchability of the tensile strain up to 600% and sensitivity of 0.685 mV %. Additionally, the flexible smart bionic fabric embedding the HOWS sensor, produced through the warp and weft knitting, significantly enhances wearing comfort. A small circuit board is prepared to enable wireless signal transmission of the designed sensor, thereby improving the daily portability. A speech recognition and human-machine interaction system, based on sensor signal acquisition, is constructed, and the convolutional neural network algorithm is integrated for disease assessment. By integrating sensing, wireless transmission, and artificial intelligence (AI), a smart tele-healthcare system based on HOWS sensors is demonstrated to hold great potential for early warning and rehabilitation monitoring of neurological diseases.

Authors

  • Tianliang Li
    Department of Biomedical Engineering, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore.
  • Qian'ao Wang
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.
  • Zichun Cao
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.
  • Jianglin Zhu
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.
  • Nian Wang
    Institute of Computing Technology(ICT), Chinese Academy of Sciences(CAS), Beijing, China.
  • Run Li
    School of Nursing, Shanxi University of Chinese Medicine, Shanxi, Taiyuan 030024, China.
  • Wei Meng
  • Quan Liu
    Vanderbilt University, Nashville, TN 37212, USA.
  • Shifan Yu
    School of Electronic Science and Engineering, Xiamen University, Xiamen, Fujian, 361005, China.
  • Xinqin Liao
    School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore. liaoxinqin677@163.com.
  • Aiguo Song
    School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P.R. China.
  • Yuegang Tan
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.
  • Zude Zhou
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, 430070, China.

Keywords

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