Enhancement of Physiological Stress Classification using Psychometric Features

Karl Magtibay, Xavier Fernando, Karthikeyan Umapathy

2022

Abstract

Psychological data features are underutilized in many acute stress studies since they are challenging to replicate and validate due to their inherent subjectivity. However, psychology and perception play essential roles in stress research according to the well-established allostatic load model. Therefore, we demonstrate the importance of accounting for psychological data in acute stress research in an ambulatory setting through a joint analysis. We enhanced stress classification by combining psychometric features with standard physiological signal features. We used the publicly available Wearable Stress and Affect Database (WESAD), from which we obtained physiological signals and psychological self-assessments from 15 participants. For each participant, a set of physiologically relevant features were extracted from each signal type. In parallel, we adapted psychometric features, positive emotion (PEscore) and negative emotion (NEscores) scores, by calculating the weighted average of self-evaluation scores. Using a stepwise feature selection and a linear- discriminant-analysis-based classifier, we found that PEscores, along with select physiological signal features, could enhance cross-validated stress classification accuracy by 8%, higher than a previous benchmark study using the same dataset. More importantly, we found that such a classification accuracy could be achieved with significantly fewer physiological signal features (by 20 times) with the aid of a psychometric feature. Finally, we found that psychometric features could indicate the type of perceived stress relating to an individual’s mood descriptor scores. Thus, a combination of psychometric and physiological data could be beneficial towards improving the detection and management of stress and support the development of holistic stress models.

Paper Citation

in Harvard Style

Magtibay K., Fernando X. and Umapathy K. (2022). Enhancement of Physiological Stress Classification using Psychometric Features. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 430-437. DOI: 10.5220/0010823300003123

in Bibtex Style

@conference{healthinf22,
author={Karl Magtibay and Xavier Fernando and Karthikeyan Umapathy},
title={Enhancement of Physiological Stress Classification using Psychometric Features},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={430-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010823300003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - Enhancement of Physiological Stress Classification using Psychometric Features
SN - 978-989-758-552-4
AU - Magtibay K.
AU - Fernando X.
AU - Umapathy K.
PY - 2022
SP - 430
EP - 437
DO - 10.5220/0010823300003123
PB - SciTePress