Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor

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 arteria...

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Bibliographic Details
Published in:Advanced materials (Weinheim) Vol. 33; no. 41; pp. e2104178 - n/a
Main Authors: Fang, Yunsheng, Zou, Yongjiu, Xu, Jing, Chen, Guorui, Zhou, Yihao, Deng, Weili, Zhao, Xun, Roustaei, Mehrdad, Hsiai, Tzung K., Chen, Jun
Format: Journal Article
Language:English
Published: Germany Wiley Subscription Services, Inc 01.10.2021
Subjects:
Algorithms
Biomonitoring
Blood Pressure
carbon nanotubes
Cardiovascular Diseases - diagnosis
Cardiovascular system
Data retrieval
Diastolic pressure
Heart - physiology
Humans
Internet of Things
Machine Learning
Materials science
Mobile Applications
Monitoring, Ambulatory - instrumentation
Monitoring, Ambulatory - methods
motion artifacts
Nanotubes, Carbon - chemistry
personalized healthcare
Perspiration
pulse wave monitoring
Response time
Sensors
Signal-To-Noise Ratio
smart textiles
Textiles
Waveforms
Wearable Electronic Devices
personalized healthcare
pulse wave monitoring
smart textiles
carbon nanotubes
motion artifacts
machine learning
ISSN:0935-9648, 1521-4095, 1521-4095
Online Access:Get full text
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Summary: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−1. 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. A waterproof textile‐based wearable cardiovascular monitoring system is developed via systematic integration of a triboelectric sensor, a signal processing circuit, a Bluetooth module, and a customized user‐friendly app interface. With the assistance of machine‐learning algorithms, this system can perform continuous blood pressure monitoring against body motion and perspiration.
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Y.F, Y.Z., and J.X. contributed equally to this work. J.C. conceived the research and supervised all aspects of the work. Y.F., Y.Z., and W.D. discussed the device structure and fabrication. Y.F. and Y.Z. fabricated the textile triboelectric sensor, conducted the measurements, simulated the electric potential distributions, and analyzed the raw data. G.C. conducted the machine learning for blood pressure measurement. Y.F., M.R., and T.K.H. conducted the blood pressure validation experiment. J.X. and X.Z. designed the wireless pulse monitoring system and developed the ”Pulse_Monitoring” APP., Y.F., and J.C. prepared the manuscript. All of the authors read, edited, and approved the manuscript.
Author Contributions
ISSN:0935-9648
1521-4095
1521-4095
DOI:10.1002/adma.202104178