Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Open accessAbstract
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 low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
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Consistent with previous studies, personality types were one of the most influential and statistically significant factors for self-reported stress and mental health in this college population.
“Consistent with previous studies [7,13,14], personality types were one of the most influential and statistically significant factors for self-reported stress and mental health in this college population.”
From Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018) · cites this paper as [7]
The Discussion (p. 7) says all measured personality traits contributed significantly to predicting daily stress, and Data Collection (p. 3) identifies the participants as graduate students who self-reported daily stress. The paper does not report a separate mental-health outcome; its finding concerns stress prediction rather than both stress and mental health. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMobile phones have been used to monitor stress and mental health.
“Mobile phones have been used to monitor stress and mental health [4-10].”
The above three studies were all performed on the "Friends and Family" data set, including 8 weeks of mobile phone data from 117 participants living in a major US university's married graduate student residency.
“The above three studies were all performed on the “Friends and Family” data set, including 8 weeks of mobile phone data from 117 participants living in a major US university’s married graduate student residency.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [18]
Bluetooth features used in previous studies [15-18] have been limited to basic statistical features (eg, sum, mean, and standard deviation), which are unable to characterize some nonlinear aspects (such as complexity, regularity, and periodicity) of the Bluetooth data.
“Besides, Bluetooth features used in these studies [15-18] have been limited to basic statistical features (eg, sum, mean, and standard deviation), which are unable to characterize some nonlinear aspects (such as complexity, regularity, and periodicity) of the Bluetooth data.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [18]
Previous studies [15-18] have been performed on relatively small (approximately 100 participants) homogeneous (eg, university students) cohorts of participants over relatively short periods (8-10 weeks), which may limit their generalizability.
“Previous studies [15-18] have been performed on relatively small (approximately 100 participants) homogeneous (eg, university students) cohorts of participants over relatively short periods (8-10 weeks), which may limit their generalizability.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [18]
Bogomolov et al established machine learning models to recognize happiness and stress with features of Bluetooth records, calls, and text messages, which obtained accuracy rates of 80.81% and 72.28%, respectively.
“Bogomolov et al established machine learning models to recognize happiness and stress with features of Bluetooth records, calls, and text messages, which obtained accuracy rates of 80.81% and 72.28%, respectively [17,18].”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [18]
Our study was performed on a larger (N=316) multicenter data set with a longer follow-up (median 4 months) than previous Bluetooth-related studies.
“Compared with previous Bluetooth-related studies [15-18], our study was performed on a larger (N=316) multicenter data set with a longer follow-up (median 4 months).”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [18]
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References 57 · Cited by 217
Research works printed but not linked: 3
- [32][32] H. Lu, D. Frauendorfer, M. Rabbi, M. S. Mast, G. T. Chittaranjan, A. T. Campbell, D. Gatica-Perez, and T. Choudhury. Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing , UbiComp '12, pages 351-360, New York, NY, USA, 2012. ACM. -
- [35][35] G. Miller. Note on the bias of information estimates. Information theory in psychology: Problems and methods , 2(95):100, 1955. -
- [38][38] A. Muaremi, B. Arnrich, and G. Tr¨ oster. A survey on measuring happiness with smart phones. In 6th International Workshop on Ubiquitous Health and Wellness (UbiHealth 2012) , 2012. -
- Authors
- Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (.
- Affiliations
- University of Trento (IT)
- Fondazione Bruno Kessler (IT)
- MIT Media Lab (US)
- Venue
- Proceedings of the 22nd ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2014-11-03
- Pages
- 477-486
- OpenAlex
- W2096309085
- Cited by
- 217
- References
- 57
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 7 · 2023: 13 · 2022: 30 · 2021: 33 · 2020: 20 · 2019: 23 · 2018: 32 · 2017: 20 · 2016: 20 · 2015: 8
- FWCI
- 26.17
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Telecom Italia (T)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- mobile phone data, behavioral metrics, weather conditions, personality traits, low-dimensional feature space
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