International Journal of Science, Engineering and TechnologyAuthors: Mrs.S.Kalaiselvi., Mrs.S. Praveena.
Abstract: Stress significantly impacts human health, with disrupted sleep patterns being a key contributor. This study introduces a deep learning-based system using Convolutional Neural Networks (CNN) to detect stress by analyzing various sleep-related parameters, including sleep duration, interruptions, heart rate variability, and body movements. Data is gathered through wearable devices and sleep monitoring applications, providing a real-world basis for analysis. The model incorporates time-series analysis and statistical feature selection techniques to enhance its predictive accuracy. Experimental results show that CNN effectively captures complex sleep behaviors linked to stress, enabling accurate classification of stress levels. This research offers an automated and reliable solution for stress detection, supporting improved mental health through better sleep analysis.
DOI: http://doi.org/
