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KanieshE/
spark-secure

Name: spark-secure
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🏥 Smart Health Monitor

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

Quick Start (3 terminals)

bash

# 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

Architecture

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

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

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

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

KanieshE/spark-secure