Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning
Stress, a natural process affecting individuals' wellbeing, has a profound impact on overall quality of life. Researchers from diverse fields employ various technologies and methodologies to investigate it and alleviate the negative effects of this phenomenon. Wearable devices, such as smart ba...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1293513/full |
| _version_ | 1797365496287330304 |
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| author | Osman Tugay Başaran Yekta Said Can Elisabeth André Cem Ersoy |
| author_facet | Osman Tugay Başaran Yekta Said Can Elisabeth André Cem Ersoy |
| author_sort | Osman Tugay Başaran |
| collection | DOAJ |
| description | Stress, a natural process affecting individuals' wellbeing, has a profound impact on overall quality of life. Researchers from diverse fields employ various technologies and methodologies to investigate it and alleviate the negative effects of this phenomenon. Wearable devices, such as smart bands, capture physiological data, including heart rate variability, motions, and electrodermal activity, enabling stress level monitoring through machine learning models. However, labeling data for model accuracy assessment poses a significant challenge in stress-related research due to incomplete or inaccurate labels provided by individuals in their daily lives. To address this labeling predicament, our study proposes implementing Semi-Supervised Learning (SSL) models. Through comparisons with deep learning-based supervised models and clustering-based unsupervised models, we evaluate the performance of our SSL models. Our experiments show that our SSL models achieve 77% accuracy with a classifier trained on an augmented dataset prepared using the label propagation (LP) algorithm. Additionally, our deep autoencoder network achieves 76% accuracy. These results highlight the superiority of SSL models over unsupervised learning techniques and their comparable performance to supervised learning models, even with limited labeled data. By relieving the burden of labeling in daily life stress recognition, our study advances stress-related research, recognizing stress as a natural process rather than a disease. This facilitates the development of more efficient and accurate stress monitoring methods in the wild. |
| first_indexed | 2024-03-08T16:49:49Z |
| format | Article |
| id | doaj.art-281b931f23de4a6fb930a5c5526b65a7 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-03-08T16:49:49Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-281b931f23de4a6fb930a5c5526b65a72024-01-05T05:01:22ZengFrontiers Media S.A.Frontiers in Psychology1664-10782024-01-011410.3389/fpsyg.2023.12935131293513Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learningOsman Tugay Başaran0Yekta Said Can1Elisabeth André2Cem Ersoy3Computer and Communication Systems (CCS) Labs, Telecommunication Networks Group (TKN), Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, GermanyFaculty of Applied Computer Science, Institute of Computer Science, Universität Augsburg, Augsburg, GermanyFaculty of Applied Computer Science, Institute of Computer Science, Universität Augsburg, Augsburg, GermanyNETLAB Research Laboratory, Department of Computer Engineering, Bogazici University, Istanbul, TurkeyStress, a natural process affecting individuals' wellbeing, has a profound impact on overall quality of life. Researchers from diverse fields employ various technologies and methodologies to investigate it and alleviate the negative effects of this phenomenon. Wearable devices, such as smart bands, capture physiological data, including heart rate variability, motions, and electrodermal activity, enabling stress level monitoring through machine learning models. However, labeling data for model accuracy assessment poses a significant challenge in stress-related research due to incomplete or inaccurate labels provided by individuals in their daily lives. To address this labeling predicament, our study proposes implementing Semi-Supervised Learning (SSL) models. Through comparisons with deep learning-based supervised models and clustering-based unsupervised models, we evaluate the performance of our SSL models. Our experiments show that our SSL models achieve 77% accuracy with a classifier trained on an augmented dataset prepared using the label propagation (LP) algorithm. Additionally, our deep autoencoder network achieves 76% accuracy. These results highlight the superiority of SSL models over unsupervised learning techniques and their comparable performance to supervised learning models, even with limited labeled data. By relieving the burden of labeling in daily life stress recognition, our study advances stress-related research, recognizing stress as a natural process rather than a disease. This facilitates the development of more efficient and accurate stress monitoring methods in the wild.https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1293513/fullmental stresspsychophysiologicalelectrodermal activityCNN-LSTMlabel propagationdeep autoencoder |
| spellingShingle | Osman Tugay Başaran Yekta Said Can Elisabeth André Cem Ersoy Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning Frontiers in Psychology mental stress psychophysiological electrodermal activity CNN-LSTM label propagation deep autoencoder |
| title | Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning |
| title_full | Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning |
| title_fullStr | Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning |
| title_full_unstemmed | Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning |
| title_short | Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning |
| title_sort | relieving the burden of intensive labeling for stress monitoring in the wild by using semi supervised learning |
| topic | mental stress psychophysiological electrodermal activity CNN-LSTM label propagation deep autoencoder |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1293513/full |
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