Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Name
1410.5816.pdf
Description
Submitted version
Size
437.66 KB
Format
Adobe PDF
Checksum (MD5)
3cf27339ec10a77add8b81666fac15c2
Author(s)
Bogomolov, Andrey
Lepri, Bruno
Ferron, Michela
Pianesi, Fabio
Pentland, Alex (Sandy)
Date Issued
November 2014
Publisher
Association for Computing Machinery (ACM)
Citation
Bogomolov, Andrey, Lepri, Bruno, Ferron, Michela, Pianesi, Fabio and Pentland, Alex (Sandy). 2014. "Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits."
Version
Original manuscript
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 lowdimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2647868.2654933
