Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/12/12/1093 |
| _version_ | 1797461247303614464 |
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| author | Md Hasib Fakir Seong Eun Yoon Abdul Mohizin Jung Kyung Kim |
| author_facet | Md Hasib Fakir Seong Eun Yoon Abdul Mohizin Jung Kyung Kim |
| author_sort | Md Hasib Fakir |
| collection | DOAJ |
| description | 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 <i>f</i>-1 score of 98.14% and 96.33%, respectively. An individual’s thermal sensation was significantly correlated with SKT, EBT, and associated features. |
| first_indexed | 2024-03-09T17:16:42Z |
| format | Article |
| id | doaj.art-e06c90d9e534420393e8531d46c86320 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-09T17:16:42Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-e06c90d9e534420393e8531d46c863202023-11-24T13:36:24ZengMDPI AGBiosensors2079-63742022-11-011212109310.3390/bios12121093Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face MaskMd Hasib Fakir0Seong Eun Yoon1Abdul Mohizin2Jung Kyung Kim3Department of Integrative Biomedical Science and Engineering, Graduate School, Kookmin University, Seoul 02707, Republic of KoreaDepartment of Mechanical Engineering, Graduate School, Kookmin University, Seoul 02707, Republic of KoreaSchool of Mechanical Engineering, Kookmin University, Seoul 02707, Republic of KoreaDepartment of Integrative Biomedical Science and Engineering, Graduate School, Kookmin University, Seoul 02707, Republic of KoreaWearable 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 <i>f</i>-1 score of 98.14% and 96.33%, respectively. An individual’s thermal sensation was significantly correlated with SKT, EBT, and associated features.https://www.mdpi.com/2079-6374/12/12/1093wearable biosensorssmart face maskskin temperatureexhaled breath temperaturethermal sensation votemachine learning |
| spellingShingle | Md Hasib Fakir Seong Eun Yoon Abdul Mohizin Jung Kyung Kim Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask Biosensors wearable biosensors smart face mask skin temperature exhaled breath temperature thermal sensation vote machine learning |
| title | Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask |
| title_full | Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask |
| title_fullStr | Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask |
| title_full_unstemmed | Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask |
| title_short | Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask |
| title_sort | prediction of individual dynamic thermal sensation in subway commute using smart face mask |
| topic | wearable biosensors smart face mask skin temperature exhaled breath temperature thermal sensation vote machine learning |
| url | https://www.mdpi.com/2079-6374/12/12/1093 |
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