A Restful Night's Sleep: Predicting Stress with Machine Learning

The primary objective of the research is to develop a technique for monitoring individuals' stress levels as they sleep. The two primary areas of the study are the categorization of transitioning to the following stage of sleep and the analysis of heart rate variations throughout this process. Using statistical analysis, the research investigates the connection between heart rate and the progression to the following sleep stage. To determine whether there is a substantial variation in heart rate values as people transition to the following sleep stage, a T-test is specifically used. This study aids in locating any potential relationships between changes in heart rate and changes in sleep stage. This study intends to give insights into the link between sleep phases, stress levels, and heart rate patterns by integrating categorization techniques and statistical analysis. The results could help in the creation of sleep monitoring and stress management systems and further our understanding of stress detection during sleep. The research might improve sleep monitoring devices, expand our understanding of stress dynamics, and eventually better stress management techniques.