Optimizing human sleep patterns using AI-Driven Insights from Wearable data Abd behavioral analysis

Öz

Sleep optimization is highly important for an individual's general well-being; however, many traditional methods that are used in improving sleep patterns lack personalization. This research explores the feasibility of AI-based insights obtained through wearable data and behavioral analysis for optimizing human sleep patterns. We used a dataset of sleep metrics from 5,000 participants over six months to analyze sleep duration, efficiency, and disturbances using machine learning models, including Random Forest and Long Short-Term Memory (LSTM) networks. Feature selection techniques identified key determinants, such as heart rate variability, movement patterns, and sleep latency. Results indicate that AI-driven models improved sleep efficiency predictions by 23% compared to conventional heuristics. Personalized sleep recommendations reduced sleep onset latency by an average of 14 minutes and increased deep sleep duration by 18%. Compared to self-reported improvements, AI-assisted insights showed a 30% higher accuracy in predicting sleep disturbances. These findings highlight the potential of integrating AI and wearable technology for personalized sleep enhancement strategies. Future work will focus on real-time feedback mechanisms and expanding datasets to diverse demographics.