🩺 SCAM – Contextual Monitoring of Cardio-Motor Anomalies

SCAM is an intelligent IoT system that monitors physiological parameters (BPM, SpO₂) and motion (accelerometer & gyroscope) in real-time to detect anomalies such as tachycardia, bradycardia, and falls. It combines an ESP32 with sensors, a distributed backend, a Flutter Web interface deployed on Netlify, and cloud services for processing, authentication, and alert management.

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🚀 Key Features

• Read physiological data via sensors (BPM, SpO₂)

• Analyze inertial signals (accelerometer & gyroscope)

• Real-time detection of cardio-motor anomalies

• Alert management and data logging

• Flutter Web dashboard displaying:

  • BPM / SpO₂ charts
  • Detected anomalies
  • Alert history

• Secure authentication via Firebase

• IoT → Cloud communication via ESP32

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🏗️ System Architecture

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📡 IoT System Diagram

The diagram below shows the connections between the ESP32, sensors, and LCD screen.

🔌 Main Connections

MAX30102 → ESP32 (I2C)

• VIN → 3.3V
• GND → GND
• SDA → GPIO 25
• SCL → GPIO 26

MPU6050 → ESP32 (I2C)

• VCC → 3.3V
• GND → GND
• SDA → GPIO 25
• SCL → GPIO 26

LCD 16×4 → ESP32 (Custom I2C)

• VCC → 5V
• GND → GND
• SDA → GPIO 25
• SCL → GPIO 26

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🛠️ Technologies Used

Frontend

• Flutter Web
• Deployed on Netlify

Backend / Processing

• API + Python processing deployed on Render

Cloud & Data

• Firebase Authentication
• Firebase Realtime Database (live data)
• Supabase (alert storage & history)

Hardware

• ESP32
• MAX30102 sensor
• MPU6050 (IMU)

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🔗 Overall Workflow

1. ESP32 reads BPM, SpO₂, accelerometer, and gyroscope data.

2. Data is sent to the Render backend, processed, and simultaneously sent to Firebase for real-time dashboard visualization.

3. Render publishes alerts to Supabase.

4. The Flutter Web interface (Netlify) fetches and displays:

   • Real-time measurements
   • Detected alerts
   • Anomaly history

The entire pipeline operates continuously in real-time.

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simulation-finale-compresse.mp4

❤️ Team

This project was a collaboration between students from Data Analytics & Artificial Intelligence and Master of Computer Engineering & Distributed Systems, as part of the IoT & Networking and Cloud Computing modules.

Master ADIA – Data Analytics & Artificial Intelligence

• Hind ELQORACHI
• Latifa KHAIR
• Kawtar KINAD

Master IISE – Computer Engineering & Distributed Systems

• Meryam EL HEFIANE
• Jihad AHBRI
• Farah BABA

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Supervision

The project was supervised by instructors responsible for the respective modules:

• [Prof. Amine RGHIOUI], [Internet of Things]
• [Prof. Monsef BOUGHROUS], [Networking / Cloud Computing]

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📄 License

Academic project — not intended for commercial use. Ibn Zohr University - IT Center of Excellence