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Paper Title

STRESS DETECTION CLASSIFICATION USING WEARABLE DEVICES USING GRADIENT BOOSTING ALGORITHM

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Registration ID: IJNRD_304797

Published ID: IJNRD2503345

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Keywords

Academic environment, Explainable AI, Feature Selection, Machine Learning, Mental stress, Wearable Device

Abstract

Mental stress in academic environments adversely impacts students’ well-being and academic performance. Through the exploration of wearable devices, particularly the utilization of IBI-derived HRV and electrodermal activity (EDA), the research demonstrates the potential for real-time stress de tection and monitoring. The findings underscore the effec tiveness of wearable technology in identifying stress among students, offering opportunities for timely interventions to alleviate its negative effects. Moreover, this paper has delved into the efficacy of stress reduction techniques, specifically the impact of listening to meditation audio. The results in dicate that suchpractices canbevaluable tools for stress reduction, providingstudentswithaccessiblemeanstoman age their stress levels. By incorporating a hybrid feature selectionapproachwithmachine learning classifiers, fine tuned through Bayesian optimization has illustrated theef fectiveness of the proposed classification models. This hybrid features election method improved the classification accuracy by 2.61% for two-level stress classification and by 1.86% for three-level stress classification compared to existing models with out hybrid feature selection. This suggests incorporating HRV and EDA signals in wearable devices can enhance stress prediction accuracy. In the future, researchers can focus on further refining the utilization of HRV and EDA signals, which can contribute to developing more advanced and accurate wearable stress monitoring systems, there by improving the over all effectiveness of stress managemen tinterventions.

How To Cite (APA)

Dr.D.J.SAMATHA NAIDU & P.JAYALAKSHMI (March-2025). STRESS DETECTION CLASSIFICATION USING WEARABLE DEVICES USING GRADIENT BOOSTING ALGORITHM. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(3), d470-d474. https://ijnrd.org/papers/IJNRD2503345.pdf

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Paper Reg. ID: IJNRD_304797

Published Paper Id: IJNRD2503345

Research Area: Science and Technology

Author Type: Indian Author

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2503345.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2503345

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