Published on: April 2026
HUMAN STRESS DETECTION BASED ON SLEEPING HABITS USING MACHINE LEARNING ALGORITHMS
Nivasini M Harini C Jothika M Kirubalani M
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Abstract
Stress has become a major issue in modern society due to increasing workload and lifestyle changes. Traditional stress detection methods, such as questionnaires and physiological sensor-based systems, are often subjective, intrusive, and unsuitable for continuous monitoring. This paper proposes a machine learning-based approach for detecting human stress using sleeping habits as non-invasive behavioral indicators.
The system analyzes sleep parameters such as duration, efficiency, awakenings, REM sleep, deep sleep, and physiological signals. A structured pipeline consisting of data preprocessing, feature selection, and classification is implemented. The proposed model utilizes XGBoost for sleep-based stress prediction and a Deep Neural Network for text-based stress analysis.
Experimental results demonstrate improved performance in terms of accuracy, precision, recall, and F1-score. The proposed system provides a scalable, adaptive, and real-time solution for continuous mental health monitoring and early stress detection
How to Cite this Paper
M, N., C, H., M, J. & M, K. (2026). Human Stress Detection Based on Sleeping Habits using Machine Learning Algorithms. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.597
M, Nivasini, et al.. "Human Stress Detection Based on Sleeping Habits using Machine Learning Algorithms." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.597.
M, Nivasini,Harini C,Jothika M, and Kirubalani M. "Human Stress Detection Based on Sleeping Habits using Machine Learning Algorithms." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.597.
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- •Published on: Apr 28 2026
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