Unsupervised Learning for Mental Stress Detection

Dorien Huysmans, Dorien Huysmans, Elena Smets, Elena Smets, Walter De Raedt, Chris Van Hoof, Chris Van Hoof, Chris Van Hoof, Katleen Bogaerts, Katleen Bogaerts, Ilse Van Diest, Denis Helic

2018

Abstract

One of the major challenges in the field of ambulant stress detection lies in the model validation. Commonly, different types of questionnaires are used to record perceived stress levels. These only capture stress levels at discrete moments in time and are prone to subjective inaccuracies. Although, many studies have already reported such issues, a solution for these difficulties is still lacking. This paper explores the potential of unsupervised learning with Self-Organizing Maps (SOM) for stress detection. In unsupervised learning settings, the labels from perceived stress levels are not needed anymore. First, a controlled stress experiment was conducted during which relax and stress phases were alternated. The skin conductance (SC) and electrocardiogram (ECG) of test subjects were recorded. Then, the structure of the SOM was built based on a training set of SC and ECG features. A Gaussian Mixture Model was used to cluster regions of the SOM with similar characteristics. Finally, by comparison of features values within each cluster, two clusters could be associated to either relax phases or stress phases. A classification performance of 79.0% (5:16) was reached with a sensitivity of 75.6% (11:2). In the future, the goal is to transfer these first initial results from a controlled laboratory setting to an ambulant environment.

Paper Citation

in Harvard Style

Huysmans D., Smets E., De Raedt W., Van Hoof C., Bogaerts K., Van Diest I. and Helic D. (2018). Unsupervised Learning for Mental Stress Detection. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS; ISBN 978-989-758-279-0, SciTePress, pages 26-35. DOI: 10.5220/0006541100260035

in Bibtex Style

@conference{biosignals18,
author={Dorien Huysmans and Elena Smets and Walter De Raedt and Chris Van Hoof and Katleen Bogaerts and Ilse Van Diest and Denis Helic},
title={Unsupervised Learning for Mental Stress Detection},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS},
year={2018},
pages={26-35},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006541100260035},
isbn={978-989-758-279-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS
TI - Unsupervised Learning for Mental Stress Detection
SN - 978-989-758-279-0
AU - Huysmans D.
AU - Smets E.
AU - De Raedt W.
AU - Van Hoof C.
AU - Bogaerts K.
AU - Van Diest I.
AU - Helic D.
PY - 2018
SP - 26
EP - 35
DO - 10.5220/0006541100260035
PB - SciTePress