Predicting Stress Levels in Sleep Patterns using Machine Learning Models and a Smart Yoga Pillow

This research endeavours to predict stress levels in sleep patterns through the innovative utilization of a Smart Yoga Pillow (SaYoPillow) combined with machine learning models. In today’s fast-paced lifestyle, stress management is paramount for overall well-being. However, people often overlook the significance of sleep in mitigating stress. The SaYoPillow is an innovative gadget that gathers many physiological measurements while a person is sleeping, such as the frequency of snoring, the rate of breathing, body temperature, the speed of limb movement, blood oxygen levels, eye movement, duration of sleep, and heart rate. Leveraging this data, our objective is to predict stress levels for the subsequent day, thereby materializing the concept of “Smart-Sleeping”. The research addresses the gap in existing stress management methodologies by offering a proactive and personalized approach rooted in real-time physiological monitoring. Our findings demonstrate the efficacy of various machine learning models, with classifiers such as KNN, Logistic Regression, and Gaussian Naive Bayes achieving 100% accuracy in stress level prediction. The results underscore the potential of the SaYoPillow system in revolutionizing stress management practices, enabling individuals to make informed decisions to enhance their sleep quality and overall well-being.