Forecasting stress, mood, and health from daytime physiology in office workers and students
© 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-5...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021
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| Online Access: | https://hdl.handle.net/1721.1/137016 |
| _version_ | 1826202210110799872 |
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| author | Umematsu, T Sano, A Taylor, S Tsujikawa, M Picard, RW |
| author_facet | Umematsu, T Sano, A Taylor, S Tsujikawa, M Picard, RW |
| author_sort | Umematsu, T |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T12:03:55Z |
| format | Article |
| id | mit-1721.1/137016 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:03:55Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1370162021-11-02T03:24:45Z Forecasting stress, mood, and health from daytime physiology in office workers and students Umematsu, T Sano, A Taylor, S Tsujikawa, M Picard, RW © 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. 2021-11-01T17:59:21Z 2021-11-01T17:59:21Z 2020 2021-07-06T14:45:37Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/137016 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. en 10.1109/EMBC44109.2020.9176706 Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE MIT web domain |
| spellingShingle | Umematsu, T Sano, A Taylor, S Tsujikawa, M Picard, RW Forecasting stress, mood, and health from daytime physiology in office workers and students |
| title | Forecasting stress, mood, and health from daytime physiology in office workers and students |
| title_full | Forecasting stress, mood, and health from daytime physiology in office workers and students |
| title_fullStr | Forecasting stress, mood, and health from daytime physiology in office workers and students |
| title_full_unstemmed | Forecasting stress, mood, and health from daytime physiology in office workers and students |
| title_short | Forecasting stress, mood, and health from daytime physiology in office workers and students |
| title_sort | forecasting stress mood and health from daytime physiology in office workers and students |
| url | https://hdl.handle.net/1721.1/137016 |
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