ICU Patient Monitoring IoT System

Overview

The ICU Patient Monitoring System is a comprehensive IoT project designed to monitor and manage real-time patient data within an ICU environment.

Parts and Functionalities

Part 1: Mobile App (Flutter)

• Functionality: Real-time plotting of medical device data, connection to ESP32 via Firebase, and remote control of medical devices.
• Features:
  • Display real-time sensor data from medical devices (e.g., ECG, heart rate, temperature, SPO2).
  • Establish a Firebase connection to interact with ESP32 for data transmission.
  • View the ECG Prediction whether the user has Arrhythmia or not.
  • Enable users to control medical devices remotely, such as opening/closing lights.

Part 2: Firebase Integration

• Functionality: Acts as a bridge between the mobile app and medical devices, controls relay for lighting through ESP32, and provides Authentication and Access Control.
• Features:
  • Manages data flow between the mobile app and medical devices securely.
  • Implements Authentication and Access Control to restrict system access.
  • Controls the relay system connected to ESP32 to manage lighting conditions based on app commands.

Part 3: ESP32 Modules

• Functionality: Interfaces with medical devices, updates real-time analog data to Firebase, controls lights based on Firebase conditions, sends real-time ECG data, and streams video through TCP.
• Features:
  • Connects with various medical devices to gather real-time analog data and updates Firebase.
  • Controls lighting conditions based on Firebase conditions received from Part 2.
  • Streams real-time ECG data and video directly to the mobile app via TCP.

Part 4: Local Server for ECG Analysis

• Functionality: Analyzes ECG signals, predicts arrhythmia, retrieves signals from Firebase or ESP32 via TCP, and uploads predictions to Firebase.
• Features:
  • Fetches ECG signals for analysis from Firebase or ESP32 via TCP.
  • Utilizes prediction models to detect arrhythmia in real time.
  • Uploads predictions regarding arrhythmia detection to Firebase.

Technologies Used

Part 1: Mobile App (Flutter)

• Framework: Flutter
• Languages: Dart
• Database Integration: Firebase

Part 2: Firebase Integration

• Platform: Firebase
• Features: Real-time Database, Authentication

Part 3: ESP32 Modules

• Hardware: ESP32 microcontroller & ESP32-Cam
• Languages: C & Micropython
• Protocols: Firebase API, TCP & UDP

Part 4: Local Server for ECG Analysis

• Technologies: Python, Machine Learning Libraries
• Signal Processing: Deep Learning Models, Digital Signal Processing (DSP) Techniques

Project Structure

Remote-Tracked-Patient-monitor
├─  Flutter Mobile App
├─  Hardware
│  ├─  Camera
│  ├─  ESP32 Medical Devices
│  └─  ESP32 ECG Send
├─  Model Training
│  ├─  ArrhythmiaModel.ipynb
│  ├─  Data
│     ├─ ecg_dataset.csv
│     └─ ecg_test.csv
├─  Locl Server
│  ├─  detection.py
│  ├─  trained_model.pkl
│  └─   ecg_files
README.md