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

TL;DR

This study demonstrates that daily stress levels can be accurately recognized using behavioral data from mobile phones, weather, and personality traits, offering a non-intrusive alternative to physiological sensors.

Contribution

The paper introduces a person-independent multifactorial model that predicts daily stress using mobile activity, weather, and traits with high accuracy and low-dimensional features.

Findings

Achieved 72.28% accuracy in stress recognition

Identified key behavioral and environmental indicators with strong predictive power

Model is efficient and suitable for multimedia applications

Abstract

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.