Quantitative and Real-Time Evaluation of Human Respiration Signals with a Shape-Conformal Wireless Sensing System

Sicheng Chen, Guocheng Qian, Bernard Ghanem, Yongqing Wang, Zhou Shu, Xuefeng Zhao, Lei Yang, Xinqin Liao, Yuanjin Zheng

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)2203460
JournalAdvanced Science
StatePublished - Sep 11 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: S.C. and G.Q. contributed equally. This research is supported by the Ministry of Education, Singapore, under its MOE ARF Tier 2 (Award no. MOE2019-T2-2-179). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Ministry of Education, Singapore. This research is also supported by the Agency for Science, Technology and Research (A*STAR) under its IAF-ICP Programme ICP1900093 and the Schaeffler Hub for Advanced Research at NTU.


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