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.
@cite_27: We describe initial results from an ongoing project to use mobile phone sensors to detect stress related situations1. The questions that we address in this stage of our work in progress is whether differences between stressful and non stressful periods can be detected in information readily available on a smartphone such as location traces, BlueTooth devices seen during the day and phone call patterns. We present an experiment with 7 students who were monitored during a two week exam session (stressful situation) and the two following weeks (non stressful period). The results show that a behaviour modification can clearly be seen, although the exact interpretation and generalization requires further work and larger scale experiments.
Bauer and Lukowicz @cite_27 focused on mid-term stress detection, monitoring 7 students during a two week exam session followed by two weeks of non-stressful period. The recorded data were related to participants' mobility patterns and social interactions, and included users' location, Bluetooth proximity, phone calls and SMSes. These features allowed to detect an average behaviour modification of 53 of this study is the small number of subjects. Our multifactorial approach outperforms the approach proposed by @cite_27 although a direct comparison may be not adequate given the different focus: our approach tend to daily classify people as "not stressed" or "stressed", while Bauer and Lukowicz try to detect stressful situations.