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A Real-Time Portable IoT System for Telework Tracking

Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the m...

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Autores principales: Zhang, Yongxin, Chen, Zheng, Tian, Haoyu, Kido, Koshiro, Ono, Naoaki, Chen, Wei, Tamura, Toshiyo, Altaf-Ul-Amin, M. D., Kanaya, Shigehiko, Huang, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521791/
https://www.ncbi.nlm.nih.gov/pubmed/34713113
http://dx.doi.org/10.3389/fdgth.2021.643042
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author Zhang, Yongxin
Chen, Zheng
Tian, Haoyu
Kido, Koshiro
Ono, Naoaki
Chen, Wei
Tamura, Toshiyo
Altaf-Ul-Amin, M. D.
Kanaya, Shigehiko
Huang, Ming
author_facet Zhang, Yongxin
Chen, Zheng
Tian, Haoyu
Kido, Koshiro
Ono, Naoaki
Chen, Wei
Tamura, Toshiyo
Altaf-Ul-Amin, M. D.
Kanaya, Shigehiko
Huang, Ming
author_sort Zhang, Yongxin
collection PubMed
description Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era.
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spelling pubmed-85217912021-10-27 A Real-Time Portable IoT System for Telework Tracking Zhang, Yongxin Chen, Zheng Tian, Haoyu Kido, Koshiro Ono, Naoaki Chen, Wei Tamura, Toshiyo Altaf-Ul-Amin, M. D. Kanaya, Shigehiko Huang, Ming Front Digit Health Digital Health Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era. Frontiers Media S.A. 2021-06-10 /pmc/articles/PMC8521791/ /pubmed/34713113 http://dx.doi.org/10.3389/fdgth.2021.643042 Text en Copyright © 2021 Zhang, Chen, Tian, Kido, Ono, Chen, Tamura, Altaf-Ul-Amin, Kanaya and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Digital Health
Zhang, Yongxin
Chen, Zheng
Tian, Haoyu
Kido, Koshiro
Ono, Naoaki
Chen, Wei
Tamura, Toshiyo
Altaf-Ul-Amin, M. D.
Kanaya, Shigehiko
Huang, Ming
A Real-Time Portable IoT System for Telework Tracking
title A Real-Time Portable IoT System for Telework Tracking
title_full A Real-Time Portable IoT System for Telework Tracking
title_fullStr A Real-Time Portable IoT System for Telework Tracking
title_full_unstemmed A Real-Time Portable IoT System for Telework Tracking
title_short A Real-Time Portable IoT System for Telework Tracking
title_sort real-time portable iot system for telework tracking
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521791/
https://www.ncbi.nlm.nih.gov/pubmed/34713113
http://dx.doi.org/10.3389/fdgth.2021.643042
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