Stress Recognition Using Wearable Sensors and Mobile Phones
Abstract
In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higher-reported stress level was related to activity level, SMS and screen on/off patterns.
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Previous studies focused on only mobile phone usage or on phone usage plus wearable sensor data only during sleep and have not taken advantage of 24/7 multimodal phone plus wearable data during wake and sleep to understand behaviors and physiology for long-term study of self-reported stress and mental health.
“These previous studies focused on only mobile phone usage or on phone usage plus wearable sensor data only during sleep and have not taken advantage of 24/7 multimodal phone + wearable data during wake and sleep to understand behaviors and physiology for long-term study of self-reported stress and mental health.”
Sano et al showed 75% and 90% accuracy for 5-day and 1-month high or low stress recognition using wearable sensor and mobile phone data.
“Sano et al also investigated 5-day self-reported high or low stress recognition (N=18) and 1-month high or low stress recognition (N=66) using wearable sensor and mobile phone data; they showed 75% and 90% accuracy using leave-one participant-out or 10-fold cross-validation, respectively [13,14].”
The combination of wearable sensor and mobile phone data has also been used to study self-reported stress in daily life.
“The combination of wearable sensor and mobile phone data has also been used to study self-reported stress in daily life [11-14].”
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.”
This result agrees with one study reporting that normalized mobility entropy and location variance were negatively correlated with depression symptoms and another study reporting that mobility patterns were highly related to stress level.
“This result agrees with one study [8] reporting that normalized mobility entropy (distribution of frequency of visiting different places) and location variance were negatively correlated with depression symptoms and another study reporting that mobility patterns were highly related to stress level [13].”
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Self-citation
- Authors
- Sano, A., & Picard, R. W.
- Affiliations
- MIT Media Lab (US)
- Venue
- 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction
- Publisher
- IEEE
- Published
- 2013-09
- Pages
- 671-676
- ISSN
- 2156-8103 (ISSN-L)
- OpenAlex
- W1985541164
- Cited by
- 523
- References
- 9
- Citations by year
- 2026: 37 · 2025: 36 · 2024: 26 · 2023: 32 · 2022: 57 · 2021: 52 · 2020: 49 · 2019: 58 · 2018: 54 · 2017: 47 · 2016: 40 · 2015: 24 · 2014: 10 · 2013: 1
- FWCI
- 34.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Affective Computing and Intelligent Interaction and workshops (publishedVersion)
- 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 › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- stress detection, wearable sensors, mobile devices, skin conductance, accelerometer, mobile phone usage patterns, activity level, stress classification, physiological markers, behavioral markers, mood, sleep
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