Stress Level Prediction Using IoT‐Enabled Sleep‐Based Physiological Monitoring and Machine Learning Techniques

Stress significantly impacts human well‐being and is closely linked to sleep disturbances. This study proposes a system for detecting stress levels using physiological signals collected during sleep via IoT‐enabled sensors. Key parameters—including heart rate, respiration, skin temperature, oxygen saturation, and heart rate variability—are analyzed to detect stress‐induced variations. A seven‐level stress classification model was developed using six machine learning algorithms: support vector machine, decision tree, naïve Bayes, k‐nearest neighbors, random forest, and logistic regression. After preprocessing, these models were evaluated using accuracy, precision, recall, and F1‐score. The results demonstrate the potential of sleep‐based physiological monitoring for early stress detection, offering a noninvasive and continuous assessment method. Future work includes extended validation on larger and more diverse datasets to improve model generalizability.