Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168681
Title: Quantitative and real-time evaluation of human respiration signals with a shape-conformal wireless sensing system
Authors: Chen, Sicheng
Qian, Guocheng
Ghanem, Bernard
Wang, Yongqing
Shu, Zhou
Zhao, Xuefeng
Yang, Lei
Liao, Xinqin
Zheng, Yuanjin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Chen, S., Qian, G., Ghanem, B., Wang, Y., Shu, Z., Zhao, X., Yang, L., Liao, X. & Zheng, Y. (2022). Quantitative and real-time evaluation of human respiration signals with a shape-conformal wireless sensing system. Advanced Science, 9(32), 2203460-. https://dx.doi.org/10.1002/advs.202203460
Project: MOE2019-T2-2-17 
IAF-ICP1900093 
Journal: Advanced Science 
Abstract: Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non-invasive device for quantitative and real-time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning-based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.
URI: https://hdl.handle.net/10356/168681
ISSN: 2198-3844
DOI: 10.1002/advs.202203460
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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