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.
python iot arduino machine-learning real-time deep-learning tensorflow keras lstm heart-rate physionet fog-computing edge-computing cyber-physical-systems accelorometer streamlit wearable-sensors sleep-monitoring sleep-quality mmash-dataset
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Updated
Mar 11, 2026 - Python