An intelligent IoT solution that provides continuous cardiovascular monitoring for diabetic patients through non-invasive sensors. The system combines hardware sensors, real-time data processing, and machine learning analytics to detect anomalies and track vital sign trends.
- Multi-sensor integration (ECG, Temperature, Heart Rate, SpO2)
- Real-time vital signs monitoring and visualisation
- Machine Learning-powered analysis:
- Anomaly detection using Isolation Forest
- Trend analysis with Exponential Smoothing
- Health state classification using Random Forest
- Secure data storage with MongoDB
- Interactive web dashboard built with Streamlit
- Dual-controller system (Arduino + Raspberry Pi)
- Python-based data processing and analysis
- Web-based visualisation and monitoring interface
- Secure data transmission and storage
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Hardware: Arduino, Raspberry Pi, MAX30102, GY-906, ECG sensors
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Backend: Python, MongoDB
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Frontend: Streamlit
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ML Libraries: scikit-learn, numpy, pandas
Click on the thumbnail to view the Demonstration video
