Sleep Quality Prediction

Predicting sleep quality is a burgeoning field aiming to improve sleep health assessment and personalized interventions. Research focuses on developing accurate predictive models using diverse data sources, including wearable sensor data (accelerometers, heart rate monitors) and polysomnography, employing machine learning techniques such as convolutional neural networks, ensemble methods (like random forests), and advanced models incorporating bidirectional state-space representations. These efforts leverage feature selection to optimize model performance and identify key predictors of sleep quality, ultimately contributing to more effective diagnosis and management of sleep disorders and improved overall health outcomes.