KanieshE/spark-secure

🏥 Smart Health Monitor

Edge-AI health monitoring system with offline-first architecture, multi-signal fusion, and adaptive personal baselines.

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Quick Start (3 terminals)

# 1. Install dependencies
pip install -r requirements.txt

# 2. Train the model (run once)
python -m ml.train_model

# Terminal A — Start Flask API
python -m backend.app

# Terminal B — Run IoT simulator
python -m simulator.simulate --patient_id 1 --count 60 --anomaly_rate 0.2

# Terminal C — Launch Streamlit dashboard
streamlit run dashboard/app.py

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Architecture

IoT Sensor → Kalman Filter → Edge ML → Multi-Signal Fusion → Tiered Alert
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                                        SQLite (offline buffer)
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                                     Sync to cloud on reconnect

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Alert Levels

Level	Trigger	Action
0	No anomaly	—
1	Mild (score>0.4)	Local buzzer
2	Moderate (≥2 signals)	SMS via GSM / 2G
3	Critical (score>0.85 or 3+ signals)	Push notification

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API Endpoints

Method	Path	Description
GET	/api/patients	List all patients
POST	/api/patient	Register new patient
POST	/api/vitals	Ingest sensor reading
GET	/api/vitals/<id>	Get vitals history
GET	/api/alerts/<id>	Get anomaly alerts only
GET	/api/baseline/<id>	Get adaptive baseline

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Key Innovations

• Adaptive Personal Baseline — Welford online algorithm, no raw history stored
• Multi-Signal Fusion — Alert requires ≥2 independent signals to fire
• Kalman Filter — Real-time noise reduction before ML inference
• Offline-First — SQLite buffer + auto-sync on reconnect
• ABDM-Ready — health_id field on every patient for Ayushman Bharat integration