Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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An Innovative Multivariate Classification Model for Wearable Stress and Affect Detection (WESAD) Dataset

Authors
Bhagwanthi Bhagwanthi1, *, Dhivyasree Dhivyasree1, Anitha Anitha1, *
1Electronics and Communication Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
*Corresponding author.
*Corresponding author. Email: anithag@stjosephs.ac.in
Corresponding Authors
Bhagwanthi Bhagwanthi, Anitha Anitha
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_118How to use a DOI?
Keywords
Affect state; machine learning; self-report; Physiological information
Abstract

This research utilizes the dataset that is publicly available on a website called WESAD which provides us with standardized data for the evaluation of an individual’s emotional state. The dataset includes the physiological information of 15 individuals extracted through various electronic devices. The individual is subjected to multiple triggers and their bodily response is recorded using wearable devices. The dataset has 6 attributes out of which only 3 are chosen for computing the stress and affect state. The reason behind choosing the 3 specific attributes will be discussed in detail below. A self-report is structured by allowing the individual to communicate the change in their emotional state due to the triggers by self-assessment. This self-reported data is then compared with the model’s evaluation that is generated using multiple machine-learning algorithms.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_118How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Bhagwanthi Bhagwanthi
AU  - Dhivyasree Dhivyasree
AU  - Anitha Anitha
PY  - 2025
DA  - 2025/05/23
TI  - An Innovative Multivariate Classification Model for Wearable Stress and Affect Detection (WESAD) Dataset
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1420
EP  - 1426
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_118
DO  - 10.2991/978-94-6463-718-2_118
ID  - Bhagwanthi2025
ER  -