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Machine Learning Based Biosignals Mental Stress Detection

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Soft Computing in Data Science (SCDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1489))

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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.

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Author information

Authors and Affiliations

  1. School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, Australia

    Adel Ali Al-Jumaily

  2. College of Computing, Fahad Bin Sultan University, Tabuk, Saudi Arabia

    Adel Ali Al-Jumaily

  3. School of Biomedical Engineering, University of Technology Sydney, Ultimo, Australia

    Nafisa Matin

  4. School of Engineering, IT and Physical Sciences, Federation University Australia, Brisbane, Australia

    Azadeh Noori Hoshyar

Authors
  1. Adel Ali Al-Jumaily
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  2. Nafisa Matin
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  3. Azadeh Noori Hoshyar
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Corresponding author

Correspondence to Adel Ali Al-Jumaily .

Editor information

Editors and Affiliations

  1. Universiti Teknologi MARA, Shah Alam, Malaysia

    Azlinah Mohamed

  2. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

  3. Universiti Teknologi MARA, Shah Alam, Malaysia

    Jasni Mohamad Zain

  4. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

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Al-Jumaily, A.A., Matin, N., Hoshyar, A.N. (2021). Machine Learning Based Biosignals Mental Stress Detection. In: Mohamed, A., Yap, B.W., Zain, J.M., Berry, M.W. (eds) Soft Computing in Data Science. SCDS 2021. Communications in Computer and Information Science, vol 1489. Springer, Singapore. https://doi.org/10.1007/978-981-16-7334-4_3

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  • DOI: https://doi.org/10.1007/978-981-16-7334-4_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7333-7

  • Online ISBN: 978-981-16-7334-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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