Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we prop...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Association for Computing Machinery (ACM)
2021
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| Online Access: | https://hdl.handle.net/1721.1/137829 |
| _version_ | 1826217509688180736 |
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| author | Bogomolov, Andrey Lepri, Bruno Ferron, Michela Pianesi, Fabio Pentland, Alex (Sandy) |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Bogomolov, Andrey Lepri, Bruno Ferron, Michela Pianesi, Fabio Pentland, Alex (Sandy) |
| author_sort | Bogomolov, Andrey |
| collection | MIT |
| description | Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced lowdimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power. |
| first_indexed | 2024-09-23T17:04:44Z |
| format | Article |
| id | mit-1721.1/137829 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:04:44Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1378292022-09-29T23:33:35Z Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits Bogomolov, Andrey Lepri, Bruno Ferron, Michela Pianesi, Fabio Pentland, Alex (Sandy) Massachusetts Institute of Technology. Media Laboratory Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced lowdimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power. 2021-11-08T20:58:21Z 2021-11-08T20:58:21Z 2014-11 2019-07-26T13:51:21Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137829 Bogomolov, Andrey, Lepri, Bruno, Ferron, Michela, Pianesi, Fabio and Pentland, Alex (Sandy). 2014. "Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits." en 10.1145/2647868.2654933 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) arXiv |
| spellingShingle | Bogomolov, Andrey Lepri, Bruno Ferron, Michela Pianesi, Fabio Pentland, Alex (Sandy) Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits |
| title | Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits |
| title_full | Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits |
| title_fullStr | Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits |
| title_full_unstemmed | Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits |
| title_short | Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits |
| title_sort | daily stress recognition from mobile phone data weather conditions and individual traits |
| url | https://hdl.handle.net/1721.1/137829 |
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