Smart Sleep Monitoring System Using Machine Learning

Abstract

Sleep is a crucial component of overall human health, and many people struggle to get enough high-quality sleep. To help individuals improve their sleep quality, we propose a sleep monitoring system that uses a smartwatch and machine learning algorithms. This system gathers data on different elements related to sleep such as duration, quality, and physiological factors, which are analyzed using Naive Bayes machine learning algorithms to provide personalized insights and recommendations for improving sleep. These recommendations could include adjustments to sleep schedules, changes to sleep hygiene practices, or dietary changes that can improve sleep. By providing personalized recommendations tailored to each individual's unique sleep patterns and physiological factors, this system has the potential to be a powerful tool for improving sleep quality and overall health. With the increasing popularity of wearable technology and the growing demand for personalized health solutions, a sleep monitoring system that uses a smartwatch and machine learning algorithms could be a valuable tool for improving sleep quality and overall well-being.