Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
This provides a non-obtrusive alternative to physiological sensors for stress detection, though it is incremental in using existing behavioral metrics.
The paper tackles daily stress recognition by proposing a multifactorial statistical model that uses mobile phone activity, weather conditions, and personality traits, achieving an accuracy of 72.28% for a 2-class problem.
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.