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Zhao T, Xiao X, Wu Y, Ma J, Li Y, Lu C, Shokoohi C, Xu Y, Zhang X, Zhang Y, Ge G, Zhang G, Chen J, Zeng Y. Tracing the Flu Symptom Progression via a Smart Face Mask. Nano Lett 2023; 23:8960-8969. [PMID: 37750614 DOI: 10.1021/acs.nanolett.3c02492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Respiration and body temperature are largely influenced by the highly contagious influenza virus, which poses persistent global public health challenges. Here, we present a wireless all-in-one sensory face mask (WISE mask) made of ultrasensitive fibrous temperature sensors. The WISE mask shows exceptional thermosensitivity, excellent breathability, and wearing comfort. It offers highly sensitive body temperature monitoring and respiratory detection capabilities. Capitalizing on the advances in the Internet of Things and artificial intelligence, the WISE mask is further demonstrated by customized flexible circuitry, deep learning algorithms, and a user-friendly interface to continuously recognize the abnormalities of both the respiration and body temperature. The WISE mask represents a compelling approach to tracing flu symptom progression in a cost-effective and convenient manner, serving as a powerful solution for personalized health monitoring and point-of-care systems in the face of ongoing influenza-related public health concerns.
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Affiliation(s)
- Tienan Zhao
- College of Textiles, Donghua University, Shanghai 201620, China
| | - Xiao Xiao
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Yuchen Wu
- College of Information Science and Technology, Donghua University, Shanghai 201620, China
| | - Jiajia Ma
- College of Textiles, Donghua University, Shanghai 201620, China
| | - Ying Li
- College of Textiles, Donghua University, Shanghai 201620, China
| | - Chengyue Lu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Cyrus Shokoohi
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Yuanqiang Xu
- College of Textiles, Donghua University, Shanghai 201620, China
| | - Xiaomin Zhang
- College of Textiles, Donghua University, Shanghai 201620, China
| | - Yuze Zhang
- College of Textiles, Donghua University, Shanghai 201620, China
| | - Gang Ge
- Department of Electrical and Computer Engineering, National University of Singapore,117583, Singapore
| | - Guanglin Zhang
- College of Information Science and Technology, Donghua University, Shanghai 201620, China
| | - Jun Chen
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Yongchun Zeng
- College of Textiles, Donghua University, Shanghai 201620, China
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Fakir MH, Yoon SE, Mohizin A, Kim JK. Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask. Biosensors (Basel) 2022; 12:1093. [PMID: 36551060 PMCID: PMC9775212 DOI: 10.3390/bios12121093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/10/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Wearable sensors and machine learning algorithms are widely used for predicting an individual's thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual's thermal sensation was significantly correlated with SKT, EBT, and associated features.
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Affiliation(s)
- Md Hasib Fakir
- Department of Integrative Biomedical Science and Engineering, Graduate School, Kookmin University, Seoul 02707, Republic of Korea
| | - Seong Eun Yoon
- Department of Mechanical Engineering, Graduate School, Kookmin University, Seoul 02707, Republic of Korea
| | - Abdul Mohizin
- School of Mechanical Engineering, Kookmin University, Seoul 02707, Republic of Korea
| | - Jung Kyung Kim
- Department of Integrative Biomedical Science and Engineering, Graduate School, Kookmin University, Seoul 02707, Republic of Korea
- School of Mechanical Engineering, Kookmin University, Seoul 02707, Republic of Korea
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Zhang K, Li Z, Zhang J, Zhao D, Pi Y, Shi Y, Wang R, Chen P, Li C, Chen G, Lei IM, Zhong J. Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis. ACS Sens 2022; 7:3135-3143. [PMID: 36196484 DOI: 10.1021/acssensors.2c01628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems.
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Affiliation(s)
- Kaijun Zhang
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
| | - Zhaoyang Li
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
| | - Jianfeng Zhang
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China.,Laboratory of Electret & Its Application, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Dazhe Zhao
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
| | - Yucong Pi
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
| | - Yujun Shi
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
| | - Renkun Wang
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
| | - Peisheng Chen
- Zhuhai Hospital of Integrated Traditional Chinese & Western Medicine, Zhuhai 519000, China
| | - Chaojie Li
- Zhuhai Hospital of Integrated Traditional Chinese & Western Medicine, Zhuhai 519000, China
| | - Gangjin Chen
- Laboratory of Electret & Its Application, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Iek Man Lei
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
| | - Junwen Zhong
- Department of Electromechanical Engineering and Centre for Artificial Intelligence and Robotics, University of Macau, Macau SAR 999078, China
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Ye Z, Ling Y, Yang M, Xu Y, Zhu L, Yan Z, Chen PY. A Breathable, Reusable, and Zero-Power Smart Face Mask for Wireless Cough and Mask-Wearing Monitoring. ACS Nano 2022; 16:5874-5884. [PMID: 35298138 DOI: 10.1021/acsnano.1c11041] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We herein introduce a lightweight and zero-power smart face mask, capable of wirelessly monitoring coughs in real time and identifying proper mask wearing in public places during a pandemic. The smart face mask relies on the compact, battery-free radio frequency (RF) harmonic transponder, which is attached to the inner layer of the mask for detecting its separation from the face. Specifically, the RF transponder composed of miniature antennas and passive frequency multiplier is made of spray-printed silver nanowires (AgNWs) coated with a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) passivation layer and the recently discovered multiscale porous polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS) substrate. Unlike conventional on-chip or on-board wireless sensors, the SEBS-AgNWs/PEDOT:PSS-based RF transponder is lightweight, stretchable, breathable, and comfortable. In addition, this wireless device has excellent resilience and robustness in long-term and repeated usages (i.e., repeated placement and removal of the soft transponder on the mask). We foresee that this wireless smart face mask, providing simultaneous cough and mask-wearing monitoring, may mitigate virus-transmissive events by tracking the potential contagious person and identifying mask-wearing conditions. Moreover, the ability to wirelessly assess cough frequencies may improve diagnosis accuracy for dealing with several diseases, such as chronic obstructive pulmonary disease.
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Affiliation(s)
- Zhilu Ye
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Yun Ling
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Minye Yang
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Yadong Xu
- Department of Biomedical, Biological, and Chemical Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Liang Zhu
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
| | - Zheng Yan
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
- Department of Biomedical, Biological, and Chemical Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Pai-Yen Chen
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States
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