A Fog Computing-based real-time sleep quality monitoring system using LSTM deep learning on wearable sensor data (PPG + Accelerometer). Trained on the MMASH dataset (PhysioNet) from 22 real subjects, achieving 92.8% accuracy. Features a Streamlit live dashboard and Arduino hardware integration.

A sleep tracking dashboard using the Syncfusion MAUI Toolkit. It features a Sleep Tracker Chart, Weekly Sleep Analyzer, and Sleep Quality Indicator to visualize sleep data, analyze trends, and improve sleep quality

🌟 A simple tool to grade and track your Pokemon Sleep progress. Calculate sleep scores, track Pokemon stats, and improve your sleep quality with this fan-made companion app.

An AI-powered app to analyze and improve sleep quality.

Supplementary materials of the article "The daily costs of workaholism: A within-individual investigation on blood pressure, emotional exhaustion, and sleep disturbances"

Machine Learning project predicting sleep quality using EDA, preprocessing, and classification models.

📓 Materials of the Study: Associations Between the Duration and Quality of Sleep in Third-Trimester Pregnant Women With the Duration of Labor

AI-powered tool for analyzing sleep patterns and predicting sleep quality from daily habits and smartphone data

Analysis of functional connectivity and fatigue and sleep quality in multiple sclerosis (MS), described in Ruiz-Rizzo et al. (2022), European Journal of Neurology