Open AccessWide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities
Author(s) -
Suo Jiao,
Liu Yifan,
Wu Cong,
Chen Meng,
Huang Qingyun,
Liu Yiming,
Yao Kuanming,
Chen Yangbin,
Pan Qiqi,
Chang Xiaoyu,
Leung Alice Yeuk Lan,
Chan Hoyin,
Zhang Guanglie,
Yang Zhengbao,
Daoud Walid,
Li Xinyue,
Roy Vellaisamy A. L.,
Shen Jiangang,
Yu Xinge,
Wang Jianping,
Li Wen Jung
Publication year - 2022
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202203565
Subject(s) - wearable computer , computer science , wearable technology , bandwidth (computing) , artificial intelligence , embedded system , telecommunications
Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID‐19) even in its coming endemic phase. Therefore, deploying a “smart mask” to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure‐based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide‐bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty‐one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro‐recalls of ≈95% in both individual and generalized models. With rich high‐frequency (≈4000 Hz) information recorded, the two‐/tri‐phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra‐lightweight but high‐frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.