Article (Published version) (633 Kb) - 
Limited access to UNIGEOther version: http://ieeexplore.ieee.org/document/7515181/
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Scientific Article
unige:105122 
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Individuals’ stress assessment using human-smartphone interaction analysis |
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| Published in | IEEE Transactions on Affective Computing. 2018, vol. 9, no. 1, p. 51-65 | |
| Abstract | The increasing presence of stress in people' lives has motivated much research efforts focusing on continuous stress assessment methods of individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., they use invasive external devices, thus increasing entry costs and reducing user acceptance, or they use some of privacy-related information. This paper presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. Two different approaches are presented. One, based on smartphone gestures analysis, e.g., `tap', `scroll', `swipe' and `text writing', and evaluated in laboratory settings with 13 participants (F-measure 79-85 percent within-subject model, 70-80 percent global model); the second one based on smartphone usage analysis and tested in-the-wild with 25 participants (F-measure 77-88 percent within-subject model, 63-83 percent global model). Results show how these two methods enable an accurate stress assessment without being too intrusive, thus increasing ecological validity of the data and user acceptance. | |
| Keywords | Human-smartphone interaction — Stress — Smartphone — Affective computing — Mobile sensing — Pervasive computing | |
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| Full text |
Article (Published version) (633 Kb) - Limited access to UNIGE Other version: http://ieeexplore.ieee.org/document/7515181/ |
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| Structures | ||
| Research group | Institute of Information Service Science (ISS) | |
| Projects | FNS: PCS-OBEY FNS: MIQModel | |
| Citation (ISO format) | CIMAN, Matteo, WAC, Katarzyna. Individuals’ stress assessment using human-smartphone interaction analysis. In: IEEE Transactions on Affective Computing, 2018, vol. 9, n° 1, p. 51-65. doi: 10.1109/TAFFC.2016.2592504 https://archive-ouverte.unige.ch/unige:105122 |
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