Sleep Pattern

Sleep pattern research focuses on accurately identifying and classifying sleep stages and behaviors, aiming to improve sleep quality and diagnose sleep-related disorders. Current research employs machine learning techniques, including neural networks, support vector machines, and ensemble methods, often leveraging data from wearable sensors and sleep diaries to analyze sleep patterns in both children and adults. These advancements enable more objective and efficient sleep assessment, potentially leading to personalized interventions and improved diagnosis of conditions like Parkinson's disease, while also offering insights into the relationship between sleep and other behavioral patterns. The development of user-friendly, accurate, and explainable AI-driven tools is a key focus for translating research findings into practical applications.