Pervasive Stress Recognition for Sustainable Living
Name
percom2014_stressRecognition_bogomolov.pdf
Description
Accepted version
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200.9 KB
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Unknown
Checksum (MD5)
271a2db57db1dc422d36238afaffc893
Author(s)
Bogomolov, Andrey
Lepri, Bruno
Ferron, Michela
Pianesi, Fabio
Pentland, Alexander Sandy
Date Issued
March 2014
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Bogomolov, Andrey, Lepri, Bruno, Ferron, Michela, Pianesi, Fabio and Pentland, Alex Sandy. 2014. "Pervasive Stress Recognition for Sustainable Living."
Version
Author's final manuscript
Abstract
In this paper we provide the evidence that daily stress can be reliably recognized based on human behavior metrics derived from the mobile phone activity (call log, sms log, bluetooth interactions). We introduce an original approach for feature extraction, selection, recognition model training and discuss the experimental results based on Random Forest and Gradient Boosted Machine algorithms. Random Forest based model showed low variance comparing to the GBM-based one, thus winning the bias-variance tradeoff and preventing over-fitting, given the noisy source data. Potential impact of the technology is reducing stress and enhancing subjective well-being for sustainable living. © 2014 IEEE.
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/10.1109/percomw.2014.6815230
