research-article

Constrained Attribute Selection for Stress Detection Based on Physiological Signals

Authors:

Valentina Markova,

Todor GanchevAuthors Info & Claims

SSIP '18: Proceedings of the 2018 International Conference on Sensors, Signal and Image Processing

Pages 41 - 45

https://doi.org/10.1145/3290589.3290597

Published: 12 October 2018 Publication History

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Abstract

We present a constrained attribute selection method that makes use of feature assessment based on the Fisher's separation criterion followed by variety reduction post-processing. The post-processing incorporates task-specific constrain into the feature selection process, as this is expected to facilitate the subsequent data modeling and classification stages. Here we validate the proposed method in an experimental setup oriented towards acute stress detection based on physiological signals. The experimental results support that the proposed method brings advantage, when compared to three other cases: (i) the full set of features, (ii) a subset selected based on prior knowledge, (iii) and a subset selected based solely on Fisher's separation criterion.

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Cited By

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Ananthanagu UAgarwal P(2024)Fostering Resilience: Machine Learning Models for Student Stress Prediction in Education2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10543492(1-5)Online publication date: 5-Apr-2024
https://doi.org/10.1109/I2CT61223.2024.10543492
Kuttala RSubramanian ROruganti V(2023)Hierarchical Autoencoder Frequency Features for Stress DetectionIEEE Access10.1109/ACCESS.2023.331636511(103232-103241)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3316365
Kuttala RSubramanian ROruganti V(2023)Multimodal Hierarchical CNN Feature Fusion for Stress DetectionIEEE Access10.1109/ACCESS.2023.323754511(6867-6878)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3237545
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Index Terms

Constrained Attribute Selection for Stress Detection Based on Physiological Signals
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Health informatics

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Published In

SSIP '18: Proceedings of the 2018 International Conference on Sensors, Signal and Image Processing

October 2018

88 pages

ISBN:9781450366205

DOI:10.1145/3290589

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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CTU: Czech Technical University in Prague

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2018

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SSIP 2018

SSIP 2018: 2018 International Conference on Sensors, Signal and Image Processing

October 12 - 14, 2018

Prague, Czech Republic

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Cited By

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Ananthanagu UAgarwal P(2024)Fostering Resilience: Machine Learning Models for Student Stress Prediction in Education2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10543492(1-5)Online publication date: 5-Apr-2024
https://doi.org/10.1109/I2CT61223.2024.10543492
Kuttala RSubramanian ROruganti V(2023)Hierarchical Autoencoder Frequency Features for Stress DetectionIEEE Access10.1109/ACCESS.2023.331636511(103232-103241)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3316365
Kuttala RSubramanian ROruganti V(2023)Multimodal Hierarchical CNN Feature Fusion for Stress DetectionIEEE Access10.1109/ACCESS.2023.323754511(6867-6878)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3237545
K ROruganti V(2022)Cross Domain Features for Subject-Independent Stress Detection2022 IEEE Region 10 Symposium (TENSYMP)10.1109/TENSYMP54529.2022.9864379(1-6)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TENSYMP54529.2022.9864379
Radhika KSubramanian ROruganti V(2022)Joint Modality Features in Frequency Domain for Stress DetectionIEEE Access10.1109/ACCESS.2022.317840910(57201-57211)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3178409
K RMurthy Oruganti V(2021)Deep Multimodal Fusion for Subject-Independent Stress Detection2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence51648.2021.9377132(105-109)Online publication date: 28-Jan-2021
https://doi.org/10.1109/Confluence51648.2021.9377132
K RMurthy Oruganti V(2020)Transfer Learning for Subject-Independent Stress Detection using Physiological Signals2020 IEEE 17th India Council International Conference (INDICON)10.1109/INDICON49873.2020.9342505(1-6)Online publication date: 10-Dec-2020
https://doi.org/10.1109/INDICON49873.2020.9342505

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