SleepSynergetic Stress Predictor: A Data-Driven Approach to Anticipating Stress Levels During Sleep

Abstract

This study paper has explored the complex relationship between stress and sleep, defining stress as a condition of mental or emotional pressure influenced by many stimuli. Our focus lies in predicting stress levels during sleep using a comprehensive Stress Behavior Detection Mechanism. The mechanism employs real-time physiological signal monitoring, as well as demographic factors have been also taken into consideration. Notably, \'snoring rate\' followed by \'sleep_hours\' and ‘eye_movement are identified as pivotal indicators of stress levels during sleep, while \'limb_movement,\' and \'heart_rate\' along with ‘age’ also contribute significantly. The model encompasses various classifiers, including Logistic Regression, Decision Tree, Random Forest, SVM, KNN, and Gaussian Naive Bayes. The stress levels are classified into five categories: \"Low/Normal,\" \"Medium Low,\" \"Medium,\" \"Medium High,\" and \"High.\" The model\'s training involves a detailed analysis using confusion matrices and classification reports for each classifier. Machine learning models have been used to calculate F1-score, recall, and accuracy. In our model, random forest has outperformed all the available models having an accuracy of 97.6%. Lastly, we also discuss future directions and possible research challenges.

Introduction

This study presents SleepSynergetic Stress Predictor, a machine learning-based framework for predicting stress levels during sleep using physiological and demographic data. The research highlights the strong relationship between sleep quality and stress, emphasizing that prolonged stress during sleep can lead to cardiovascular diseases, weakened immunity, hormonal imbalance, anxiety, depression, and reduced cognitive performance. By analyzing physiological parameters such as snoring rate, sleeping hours, eye movement, heart rate, along with age, the proposed system aims to provide accurate stress prediction and support personalized health interventions. The study also identifies research gaps in existing approaches, including limited physiological features, reliance on single datasets, insufficient classifier diversity, and lack of automated, secure stress monitoring systems.

To address these limitations, the framework evaluates multiple machine learning classifiers, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), and Gaussian Naïve Bayes, using performance metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Results show that Random Forest achieved the highest prediction accuracy, outperforming the other models, while Logistic Regression delivered the lowest performance. The study's novel contributions include integrating demographic information (age), using multiple datasets for greater diversity, conducting feature importance analysis to identify key stress predictors, and comparing several classification models to improve prediction reliability. Overall, the proposed system demonstrates that combining diverse physiological indicators with advanced machine learning techniques can significantly enhance stress prediction during sleep, enabling more effective health monitoring and personalized sleep management.

Conclusion

In conclusion, SleepSynergetic\'s groundbreaking achievements in sleep science set it apart as a transformative force in stress level prediction during sleep. Departing from conventional approaches, the project relies on five distinct datasets, addressing critical limitations and ensuring a robust, universally applicable model. The inclusion of the \"Age\" factor represents a significant evolution, recognizing the nuanced influence of age on sleep patterns and stress responses, making SleepSynergetic a comprehensive and inclusive model. The project\'s meticulous approach is evident in the careful selection and prioritization of impactful features, with the snoring rate emerging as a standout contributor to stress level predictions. The commitment to precision is further demonstrated through the integration of information from diverse datasets and the utilization of a range of classifiers, ensuring versatility and adaptability across various conditions in sleep science. Looking ahead, SleepSynergetic\'s future prospects include real-time monitoring, personalized recommendations, large-scale studies, expanded feature sets, mobile application development, and longitudinal studies. These initiatives underscore the project\'s dedication to advancing sleep-related stress management and overall well-being. By empowering individuals to take charge of their sleep health and stress management, SleepSynergetic not only contributes to individual well-being but also lays the foundation for future advancements in understanding the complex relationship between sleep and stress in diverse populations. SleepSynergetic is not just a milestone in sleep science; it is a pioneering contribution that promises continued innovation and deeper insights into the intricacies of sleep patterns and stress responses.

References

[1] L. Rachakonda, S. P. Mohanty, E. Kougianos, and P. Sundaravadivel, “Stress-Lysis: A DNN-Integrated Edge Device for Stress Level Detection in the IoMT,” IEEE Trans. Conum. Electron., vol. 65, no. 4, pp. 474– 483, Nov 2019. [2] L. Rachakonda, S. P. Mohanty, and E. Kougianos, “iLog: An intelligent device for automatic food intake monitoring and stress detection in the IoMT,” IEEE Trans. Conum. Electron., vol. 2, no. 66, pp. 115–124, May 2020. [3] W. Koczkodaj, J. Masiak, M. Mazurek, D. Strzaka, and P. F. Zabrodskii, “Massive Health Record Breaches Evidenced by the Office for Civil Rights Data.” Iran J Pub. Hea., vol. 48, no. 2, pp. 278–288, 2019. [4] S. Adedoyin, W. Fernando, A. Aggoun, and K. M. Kondoz, “Motion and Disparity Estimation with Self Adapted Evolutionary Strategy in 3D Video Coding,” IEEE Trans. Conum. Electron., vol. 53, no. 4, pp. 1768–1775, 2007. [5] B. K. Wiederhold, I. T. Miller, and M. D. Wiederhold, “Using Virtual Reality to Mobilize Health Care: Mobile Virtual Reality Technology for Attenuation of Anxiety and Pain,” in Proc. IEEE Consum. Electron. Mag., vol. 7, no. 1, pp. 106–109, 2018. [6] F. Sannholm, J. Paalasmaa, and L. Leppakorpi, “System for Determining the Quality of Sleep,” US Patent 20 160 213 309A1, 2015. [7] R. K. Nath, H. Thapliyal, A. Caban-Holt, and S. P. Mohanty, “Machine Learning based Solutions for Real-Time Stress Monitoring,” IEEE Consum. Electron. Mag., pp. 1–1, 2020. [8] S. Lin, H. Zhang, Y. Gao, M. Du, and M. Vai, “The Effects of Muscle Stress on Signal Transmission in the Intra-Body Communication,” in Proc. IEEE Int. Conf. Consum. Electron. - China (ICCE-China), 2016, pp. 1–3. [9] N. Schneiderman, G. Ironson, and S. Siegel, “Stress and Health: Psychological, Behavioral, and Biological Determinants.” Annu Rev Clin Psychol, vol. 1, p. 607628, 2005. [10] D. Kim, H. Han, and R. Park, “Gradient Information-Based Image Quality Metric,” IEEE Trans. Conum. Electron., vol. 56, no. 2, pp. 930– 936, 2010. [11] T. Kawase, M. Okamoto, T. Fukutomi, and Y. Takahashi, “Speech Enhancement Parameter Adjustment to Maximize Accuracy of Automatic Speech Recognition,” IEEE Trans. Conum. Electron., vol. 66, no. 2, pp. 125–133, 2020. [12] Oded Z Maimon and Lior Rokach. Data mining with decision trees: theory and applications, volume 81. World scientific, 2014. [13] Priyanka and Dharmender Kumar. Decision tree classifier: A detailed survey. International Journal of Information and Decision Sciences, 12(3):246–269, 2020. [14] Quinlan, jr: C4. 5 programs for machine learning, morgan kaufmann, san mateo, california (1992). [15] LU, Y. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130-135. [16] Kim, H., Pang, S., Je, H., Kim, D., & Yang Bang, S. (2003). Constructing support vector machine ensemble. Pattern Recognition, 36(12), 2757-2767. [17] Leo Breiman. Random forests–random features. technical report 567, statistics department, university of california, berkeley,. 1999. [18] Leo Breiman. Bagging predictors. Machine learning, 24(2):123–140, 1996. [19] J Ross Quinlan. C4. 5: programs for machine learning. Elsevier, 2014. [20] Logistic regression. (2023, December 19). In Wikipedia. https://en.wikipedia.org/wiki/Logistic_regression [21] Zhang, M., & Zhou, Z. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7), 2038-2048. https://doi.org/10.1016/j.patcog.2006.12.01

Copyright

Copyright © 2026 Anjali Choudhary. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.