Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date: Oct 1, 2021

Abstract

Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can convert subtle skin deformation caused by arterial pulsatility into electricity for high-fidelity and continuous pulse waveform monitoring in an ambulatory and sweaty setting is developed. The sensor holds a signal-to-noise ratio of 23.3 dB, a response time of 40 ms, and a sensitivity of 0.21 µA kPa . With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital. Additionally, a customized cellphone application (APP) based on built-in algorithm is developed for one-click health data sharing and data-driven cardiovascular diagnosis. The textile triboelectric sensor enabled wireless biomonitoring system is expected to offer a practical paradigm for continuous and personalized cardiovascular system characterization in the era of the Internet of Things.

Authors

  • Yunsheng Fang
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Yongjiu Zou
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Jing Xu
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Guorui Chen
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Yihao Zhou
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Weili Deng
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
  • Xun Zhao
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Mehrdad Roustaei
  • Tzung K Hsiai
    School of Medicine, University of California Los Angeles, Los Angeles, CA 90073, USA. THsiai@mednet.ucla.edu.
  • Jun Chen
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.

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