Forecasting stress, mood, and health from daytime physiology in office workers and students
Name
EMBC20_1345_FI.pdf
Description
Accepted version
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226.29 KB
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Unknown
Checksum (MD5)
6f3bcdd680a5ffe9614337cae2e7d33b
Author(s)
Umematsu, T
Sano, Akane
Taylor, Sara Ann
Tsujikawa, M
Picard, Rosalind W.
Date Issued
2020
Journal
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher
IEEE
Citation
Umematsu, T, Sano, A, Taylor, S, Tsujikawa, M and Picard, RW. 2020. "Forecasting stress, mood, and health from daytime physiology in office workers and students." Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July.
Version
Author's final manuscript
Abstract
© 2020 IEEE. We examine the problem of forecasting tomorrow morning's three self-reported levels (on scales from 0 to 100) of stressed-calm, sad-happy, and sick-healthy based on physiological data (skin conductance, skin temperature, and acceleration) from a sensor worn on the wrist from 10am-5pm today. We train automated forecasting regression algorithms using Random Forests and compare their performance over two sets of data: workers consisting of 490 days of weekday data from 39 employees at a high-tech company in Japan and students consisting of 3,841 days of weekday data from 201 New England USA college students. Mean absolute errors on held-out test data achieved 10.8, 13.5, and 14.4 for the estimated levels of mood, stress, and health respectively of office workers, and 17.8, 20.3, and 20.4 for the mood, stress, and health respectively of students. Overall the two groups reported comparable stress and mood scores, while employees reported slightly poorer health, and engaged in significantly lower levels of physical activity as measured by accelerometers. We further examine differences in population features and how systems trained on each population performed when tested on the other.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/EMBC44109.2020.9176706
