Application of Dimension Reduction Methods for Stress Detection
Effective detection of stress situations plays an important role in combating it. This is the main source of motivation for research to identify and evaluate different psychological conditions. Different monitor signals are used to identify individuals' stress situations in daily life. Electroe...
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| Format: | Article |
| Language: | English |
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levent
2023-12-01
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| Series: | International Journal of Pioneering Technology and Engineering |
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| Online Access: | https://ijpte.com/index.php/ijpte/article/view/56 |
| _version_ | 1827311045780176896 |
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| author | Erhan Bergil |
| author_facet | Erhan Bergil |
| author_sort | Erhan Bergil |
| collection | DOAJ |
| description | Effective detection of stress situations plays an important role in combating it. This is the main source of motivation for research to identify and evaluate different psychological conditions. Different monitor signals are used to identify individuals' stress situations in daily life. Electroencephalogram (EEG) signals are the main component used to detect stress and depression. The long-term acquisition of this signals partially interrupts daily life and negatively affects it. Researchers are trying to develop wearable technologies that can eliminate this disadvantage. In this study, stress situations are detected utilizing different sensors without EEG signals. The achievements of three different classification methods for different dimensional feature spaces have been compared. The effects of the feature selection and dimension reduction methods on the system performance have been analyzed. During the dimension reduction process, Minimum Redundancy Maximum Relevance (MRMR), Anova, Chi-2, Relieff, Kruskal Wallis (KW) and Principal Component Analysis (PCA) methods are implemented. Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) methods are used as classifiers. The best performance is achieved with 96.2 % accuracy in 15-dimensional by using LDA and PCA methods together. |
| first_indexed | 2024-04-24T20:10:43Z |
| format | Article |
| id | doaj.art-2b27d8aa010a4146979265e0cecf1a11 |
| institution | Directory Open Access Journal |
| issn | 2822-454X |
| language | English |
| last_indexed | 2024-04-24T20:10:43Z |
| publishDate | 2023-12-01 |
| publisher | levent |
| record_format | Article |
| series | International Journal of Pioneering Technology and Engineering |
| spelling | doaj.art-2b27d8aa010a4146979265e0cecf1a112024-03-23T09:04:17ZengleventInternational Journal of Pioneering Technology and Engineering2822-454X2023-12-0120217618010.56158/jpte.2023.56.2.0256Application of Dimension Reduction Methods for Stress DetectionErhan Bergil0https://orcid.org/0000-0002-6526-1661Faculty of Engineering, Department of Electrical Electronics Engineering, Amasya University, Amasya, TürkiyeEffective detection of stress situations plays an important role in combating it. This is the main source of motivation for research to identify and evaluate different psychological conditions. Different monitor signals are used to identify individuals' stress situations in daily life. Electroencephalogram (EEG) signals are the main component used to detect stress and depression. The long-term acquisition of this signals partially interrupts daily life and negatively affects it. Researchers are trying to develop wearable technologies that can eliminate this disadvantage. In this study, stress situations are detected utilizing different sensors without EEG signals. The achievements of three different classification methods for different dimensional feature spaces have been compared. The effects of the feature selection and dimension reduction methods on the system performance have been analyzed. During the dimension reduction process, Minimum Redundancy Maximum Relevance (MRMR), Anova, Chi-2, Relieff, Kruskal Wallis (KW) and Principal Component Analysis (PCA) methods are implemented. Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) methods are used as classifiers. The best performance is achieved with 96.2 % accuracy in 15-dimensional by using LDA and PCA methods together.https://ijpte.com/index.php/ijpte/article/view/56feature selectiondimension reductionstress detection |
| spellingShingle | Erhan Bergil Application of Dimension Reduction Methods for Stress Detection International Journal of Pioneering Technology and Engineering feature selection dimension reduction stress detection |
| title | Application of Dimension Reduction Methods for Stress Detection |
| title_full | Application of Dimension Reduction Methods for Stress Detection |
| title_fullStr | Application of Dimension Reduction Methods for Stress Detection |
| title_full_unstemmed | Application of Dimension Reduction Methods for Stress Detection |
| title_short | Application of Dimension Reduction Methods for Stress Detection |
| title_sort | application of dimension reduction methods for stress detection |
| topic | feature selection dimension reduction stress detection |
| url | https://ijpte.com/index.php/ijpte/article/view/56 |
| work_keys_str_mv | AT erhanbergil applicationofdimensionreductionmethodsforstressdetection |