Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities
Abstract 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 present...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-11-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202203565 |
| _version_ | 1811331583126274048 |
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| author | Jiao Suo Yifan Liu Cong Wu Meng Chen Qingyun Huang Yiming Liu Kuanming Yao Yangbin Chen Qiqi Pan Xiaoyu Chang Alice Yeuk Lan Leung Ho‐yin Chan Guanglie Zhang Zhengbao Yang Walid Daoud Xinyue Li Vellaisamy A. L. Roy Jiangang Shen Xinge Yu Jianping Wang Wen Jung Li |
| author_facet | Jiao Suo Yifan Liu Cong Wu Meng Chen Qingyun Huang Yiming Liu Kuanming Yao Yangbin Chen Qiqi Pan Xiaoyu Chang Alice Yeuk Lan Leung Ho‐yin Chan Guanglie Zhang Zhengbao Yang Walid Daoud Xinyue Li Vellaisamy A. L. Roy Jiangang Shen Xinge Yu Jianping Wang Wen Jung Li |
| author_sort | Jiao Suo |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T16:22:53Z |
| format | Article |
| id | doaj.art-5ad8fbddc510456fb54e426bbf980441 |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-04-13T16:22:53Z |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-5ad8fbddc510456fb54e426bbf9804412022-12-22T02:39:49ZengWileyAdvanced Science2198-38442022-11-01931n/an/a10.1002/advs.202203565Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory ActivitiesJiao Suo0Yifan Liu1Cong Wu2Meng Chen3Qingyun Huang4Yiming Liu5Kuanming Yao6Yangbin Chen7Qiqi Pan8Xiaoyu Chang9Alice Yeuk Lan Leung10Ho‐yin Chan11Guanglie Zhang12Zhengbao Yang13Walid Daoud14Xinyue Li15Vellaisamy A. L. Roy16Jiangang Shen17Xinge Yu18Jianping Wang19Wen Jung Li20Dept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Biomedical Engineering City University of Hong Kong Hong Kong ChinaDept. of Biomedical Engineering City University of Hong Kong Hong Kong ChinaDept. of Computer Science City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaSchool of Chinese Medicine The University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaSchool of Data Science City University of Hong Kong Hong Kong ChinaJames Watt School of Engineering University of Glasgow Scotland UKSchool of Chinese Medicine The University of Hong Kong Hong Kong ChinaDept. of Biomedical Engineering City University of Hong Kong Hong Kong ChinaDept. of Computer Science City University of Hong Kong Hong Kong ChinaDept. of Mechanical Engineering City University of Hong Kong Hong Kong ChinaAbstract 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.https://doi.org/10.1002/advs.202203565Covid‐19high‐frequency pressure sensorsrespiratory sounds recognitionsmart maskssponge structure sensors |
| spellingShingle | Jiao Suo Yifan Liu Cong Wu Meng Chen Qingyun Huang Yiming Liu Kuanming Yao Yangbin Chen Qiqi Pan Xiaoyu Chang Alice Yeuk Lan Leung Ho‐yin Chan Guanglie Zhang Zhengbao Yang Walid Daoud Xinyue Li Vellaisamy A. L. Roy Jiangang Shen Xinge Yu Jianping Wang Wen Jung Li Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities Advanced Science Covid‐19 high‐frequency pressure sensors respiratory sounds recognition smart masks sponge structure sensors |
| title | Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities |
| title_full | Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities |
| title_fullStr | Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities |
| title_full_unstemmed | Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities |
| title_short | Wide‐Bandwidth Nanocomposite‐Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities |
| title_sort | wide bandwidth nanocomposite sensor integrated smart mask for tracking multiphase respiratory activities |
| topic | Covid‐19 high‐frequency pressure sensors respiratory sounds recognition smart masks sponge structure sensors |
| url | https://doi.org/10.1002/advs.202203565 |
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