TY - GEN
T1 - Toward the Analysis of Office Workers’ Mental Indicators Based on Wearable, Work Activity, and Weather Data
AU - Nishimura, Yusuke
AU - Hossain, Tahera
AU - Sano, Akane
AU - Isomura, Shota
AU - Arakawa, Yutaka
AU - Inoue, Sozo
N1 - Funding Information:
The experiment of this research was carried out in collaboration with companies and universities participating in the “2020 Sensing & Transformation Study Group”, whose secretariat is the Applied Brain Science Consortium.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In recent years, many organizations have prioritized efforts to detect and treat mental health issues. In particular, office workers are affected by many stressors, and physical and mental exhaustion, which is also a social problem. To improve the psychological situation in the workplace, we need to clarify the cause. In this paper, we conducted a 14-day experiment to collect wristband sensor data as well as behavioral and psychological questionnaire data from about 100 office workers. We developed machine learning models to predict psychological indexes using the data. In addition, we analyzed the correlation between behavior (work content and work environment) and psychological state of office workers to reveal the relationship between their work content, work environment, and behavior. As a result, we showed that multiple psychological indicators of office workers can be predicted with more than 80% accuracy using wearable sensors, behavioral data, and weather data. Furthermore, we found that in the working environment, the time spent in “web conferencing”, “working at home (living room)”, and “break time (work time)’ had a significant effect on the psychological state of office workers.
AB - In recent years, many organizations have prioritized efforts to detect and treat mental health issues. In particular, office workers are affected by many stressors, and physical and mental exhaustion, which is also a social problem. To improve the psychological situation in the workplace, we need to clarify the cause. In this paper, we conducted a 14-day experiment to collect wristband sensor data as well as behavioral and psychological questionnaire data from about 100 office workers. We developed machine learning models to predict psychological indexes using the data. In addition, we analyzed the correlation between behavior (work content and work environment) and psychological state of office workers to reveal the relationship between their work content, work environment, and behavior. As a result, we showed that multiple psychological indicators of office workers can be predicted with more than 80% accuracy using wearable sensors, behavioral data, and weather data. Furthermore, we found that in the working environment, the time spent in “web conferencing”, “working at home (living room)”, and “break time (work time)’ had a significant effect on the psychological state of office workers.
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U2 - 10.1007/978-981-19-0361-8_1
DO - 10.1007/978-981-19-0361-8_1
M3 - Conference contribution
AN - SCOPUS:85130238679
SN - 9789811903601
T3 - Smart Innovation, Systems and Technologies
SP - 1
EP - 26
BT - Sensor- and Video-Based Activity and Behavior Computing - Proceedings of 3rd International Conference on Activity and Behavior Computing ABC 2021
A2 - Ahad, Md Atiqur
A2 - Inoue, Sozo
A2 - Roggen, Daniel
A2 - Fujinami, Kaori
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Activity and Behavior Computing, ABC 2021
Y2 - 22 October 2021 through 23 October 2021
ER -