Advancing Stress Detection with Machine Learning: A Study on Multimodal Data Integration
Stress detection is a critical area in mental health, impacting both individual well-being and productivity. Traditional methods of stress assessment are often subjective and time-consuming. Recent advancements in machine learning have opened new avenues for automatic stress detection using multimod...
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| Veröffentlicht in: | International journal of communication networks and information security 2024-09, Vol.16 (3), p.174-177 |
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| container_title | International journal of communication networks and information security |
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| creator | Gupta, Reshu Kumar, Aniket |
| description | Stress detection is a critical area in mental health, impacting both individual well-being and productivity. Traditional methods of stress assessment are often subjective and time-consuming. Recent advancements in machine learning have opened new avenues for automatic stress detection using multimodal data fusion techniques, which integrate diverse data sources such as physiological signals, behavioral cues, and contextual information. This paper explores the state-of-the-art multimodal data fusion techniques for automatic stress detection, presenting a comprehensive literature review, detailed methodology, and an analysis of their efficacy. The study concludes by discussing the challenges, potential solutions, and future directions in the field. |
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| ispartof | International journal of communication networks and information security, 2024-09, Vol.16 (3), p.174-177 |
| issn | 2073-607X 2076-0930 |
| language | eng |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Accuracy Algorithms Data collection Data integration Datasets Feature selection Heart rate Literature reviews Machine learning Mental health Neural networks Performance evaluation Physiology Principal components analysis Questionnaires Sensors Smartphones State-of-the-art reviews Stress Support vector machines |
| title | Advancing Stress Detection with Machine Learning: A Study on Multimodal Data Integration |
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