Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the...

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Autores principales:	Lei Zhang, Yanjin Zhu, Mingliang Jiang, Yuchen Wu, Kailian Deng, Qin Ni
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	human activity recognition (HAR) wearable sensors COVID-19 temperature sensor machine learning (ML) Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ece40c4c1a9540c5b3c1fb0bd10fa1c9
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spelling	oai:doaj.org-article:ece40c4c1a9540c5b3c1fb0bd10fa1c92021-11-25T18:57:18ZBody Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition10.3390/s212275401424-8220https://doaj.org/article/ece40c4c1a9540c5b3c1fb0bd10fa1c92021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7540https://doaj.org/toc/1424-8220Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.Lei ZhangYanjin ZhuMingliang JiangYuchen WuKailian DengQin NiMDPI AGarticlehuman activity recognition (HAR)wearable sensorsCOVID-19temperature sensormachine learning (ML)Chemical technologyTP1-1185ENSensors, Vol 21, Iss 7540, p 7540 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	human activity recognition (HAR) wearable sensors COVID-19 temperature sensor machine learning (ML) Chemical technology TP1-1185
spellingShingle	human activity recognition (HAR) wearable sensors COVID-19 temperature sensor machine learning (ML) Chemical technology TP1-1185 Lei Zhang Yanjin Zhu Mingliang Jiang Yuchen Wu Kailian Deng Qin Ni Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition
description	Existing wearable systems that use G-sensors to identify daily activities have been widely applied for medical, sports and military applications, while body temperature as an obvious physical characteristic that has rarely been considered in the system design and relative applications of HAR. In the context of the normalization of COVID-19, the prevention and control of the epidemic has become a top priority. Temperature monitoring plays an important role in the preliminary screening of the population for fever. Therefore, this paper proposes a wearable device embedded with inertial and temperature sensors that is used to apply human behavior recognition (HAR) to body surface temperature detection for body temperature monitoring and adjustment by evaluating recognition algorithms. The sensing system consists of an STM 32-based microcontroller, a 6-axis (accelerometer and gyroscope) sensor, and a temperature sensor to capture the original data from 10 individual participants under 4 different daily activity scenarios. Then, the collected raw data are pre-processed by signal standardization, data stacking and resampling. For HAR, several machine learning (ML) and deep learning (DL) algorithms are implemented to classify the activities. To compare the performance of different classifiers on the seven-dimensional dataset with temperature sensing signals, evaluation metrics and the algorithm running time are considered, and random forest (RF) is found to be the best-performing classifier with 88.78% recognition accuracy, which is higher than the case of the absence of temperature data (<78%). In addition, the experimental results show that participants’ body surface temperature in dynamic activities was lower compared to sitting, which can be associated with the possible missing fever population due to temperature deviations in COVID-19 prevention. According to different individual activities, epidemic prevention workers are supposed to infer the corresponding standard normal body temperature of a patient by referring to the specific values of the mean expectation and variance in the normal distribution curve provided in this paper.
format	article
author	Lei Zhang Yanjin Zhu Mingliang Jiang Yuchen Wu Kailian Deng Qin Ni
author_facet	Lei Zhang Yanjin Zhu Mingliang Jiang Yuchen Wu Kailian Deng Qin Ni
author_sort	Lei Zhang
title	Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition
title_short	Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition
title_full	Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition
title_fullStr	Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition
title_full_unstemmed	Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition
title_sort	body temperature monitoring for regular covid-19 prevention based on human daily activity recognition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ece40c4c1a9540c5b3c1fb0bd10fa1c9
work_keys_str_mv	AT leizhang bodytemperaturemonitoringforregularcovid19preventionbasedonhumandailyactivityrecognition AT yanjinzhu bodytemperaturemonitoringforregularcovid19preventionbasedonhumandailyactivityrecognition AT mingliangjiang bodytemperaturemonitoringforregularcovid19preventionbasedonhumandailyactivityrecognition AT yuchenwu bodytemperaturemonitoringforregularcovid19preventionbasedonhumandailyactivityrecognition AT kailiandeng bodytemperaturemonitoringforregularcovid19preventionbasedonhumandailyactivityrecognition AT qinni bodytemperaturemonitoringforregularcovid19preventionbasedonhumandailyactivityrecognition
_version_	1718410499022913536

Body Temperature Monitoring for Regular COVID-19 Prevention Based on Human Daily Activity Recognition

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