Stress Recognition - A Step Outside the Lab

Julian Ramos, Jin-Hyuk Hong, Anind K. Dey


Despite the potential for stress and emotion recognition outside the lab environment, very little work has been reported that is feasible for use in the real world and much less for activities involving physical activity. In this work, we move a step forward towards a stress recognition system that works on a close to real world data set and shows a significant improvement over classification only systems. Our method uses clustering to separate the data into physical exertion levels and later performs stress classification over the discovered clusters. We validate our approach on a physiological stress dataset from 20 participants who performed 3 different activities of varying intensity under 3 different types of stimuli intended to cause stress. The results show an f-measure improvement of 130\% compared to using classification only.


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Paper Citation

in Harvard Style

Ramos J., Hong J. and K. Dey A. (2014). Stress Recognition - A Step Outside the Lab . In Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-006-2, pages 107-118. DOI: 10.5220/0004725701070118

in Bibtex Style

author={Julian Ramos and Jin-Hyuk Hong and Anind K. Dey},
title={Stress Recognition - A Step Outside the Lab},
booktitle={Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,},

in EndNote Style

JO - Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Stress Recognition - A Step Outside the Lab
SN - 978-989-758-006-2
AU - Ramos J.
AU - Hong J.
AU - K. Dey A.
PY - 2014
SP - 107
EP - 118
DO - 10.5220/0004725701070118