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Research has proven that stress reduces quality of life and causes manydiseases. For this reason, several researchers devised stress detection systemsbased on physiological parameters. However, these systems require thatobtrusive sensors are continuously carried by the user. In our paper, wepropose an alternative approach providing evidence that daily stress can bereliably recognized based on behavioral metrics, derived from the user's mobilephone activity and from additional indicators, such as the weather conditions(data pertaining to transitory properties of the environment) and thepersonality traits (data concerning permanent dispositions of individuals). Ourmultifactorial statistical model, which is person-independent, obtains theaccuracy score of 72.28% for a 2-class daily stress recognition problem. Themodel is efficient to implement for most of multimedia applications due tohighly reduced low-dimensional feature space (32d). Moreover, we identify anddiscuss the indicators which have strong predictive power.

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits