- Title
- Machine learning based biosignals mental stress detection
- Creator
- Al-Jumaily, Adel; Matin, Nafisa; Hoshyar, Azadeh
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180934
- Identifier
- vital:15853
- Identifier
-
https://doi.org/10.1007/978-981-16-7334-4_3
- Identifier
- ISBN:1865-0929 (ISSN); 9789811673337 (ISBN)
- Abstract
- Mental Stress can be defined as a normal physiological and biological reaction to an incident or a situation that makes a person feel challenged, troubled, or helpless. While dealing with stress, some changes occur in the biological function of a person, which results in a considerable change in some bio-signals such as, Electrocardiogram (ECG), Electromyography (EMG), Electrodermal Activity (EDA), respiratory rate. This paper aims to review the effect of mental stress on mental condition and health, the changes in biosignals as an indicator of the stress response and train a model to detect stressed states using the biosignals. This paper delivers a brief review of mental stress and biosignals correlation. It represents four Support Vector Machine (SVM) models trained with ECG and EMG features from an open access database based on task related stress. After performing comparative analysis on the four types of trained SVM models with chosen features, Gaussian Kernel SVM is selected as the best SVM model to detect mental stress which can predict the mental condition of a subject for a stressed and relaxed condition having an accuracy of 93.7%. These models can be investigated further with more biosignals and applied in practice, which will be beneficial for the physician. © 2021, Springer Nature Singapore Pte Ltd.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 6th International Conference on Soft Computing in Data Science, SCDS 2021 Vol. 1489 CCIS, p. 28-41
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021, Springer Nature Singapore Pte Ltd
- Subject
- ECG; EMG; Gaussian Kernel SVM; Machine learning; Mental stress; Support Vector Machine
- Reviewed
- Hits: 409
- Visitors: 345
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|