The Health Monitoring System project aims to create a comprehensive platform for real-time health monitoring using various sensors. It integrates data from temperature, accelerometer, and heart rate sensors, processes this data through a Flask API, and visualizes it in a Flutter application. The system is designed to provide timely health insights and alerts based on machine learning predictions.
The primary purpose is to develop a health monitoring system that offers users immediate access to their health data and predictive analytics, enhancing personal health management.
The project encompasses hardware setup, software architecture, machine learning model development, API integration, and mobile application development. It includes data collection, transmission, processing, and visualization of health parameters in an intuitive interface.
This documentation is targeted at developers, healthcare professionals, and stakeholders interested in the technical implementation and functionality of the health monitoring system.
- DS18B20: Temperature sensor
- MPU-6050: Accelerometer
- MAX 30102: Heart rate sensor
The ESP8266 is configured as the central device to collect data from the sensors and transmit it to the cloud.
- WiFi Connection: The ESP8266 is configured to connect to the local Wi-Fi network.
- Sensor Connections: The sensors are connected to the ESP8266 according to the manufacturer's specifications.
- Libraries: Necessary libraries (e.g., Adafruit Sensor library) are installed on the ESP8266 for sensor data acquisition.
The software architecture consists of a Flask API that processes incoming sensor data and a Flutter application that serves as the user interface.
The system integrates hardware sensors with software components through communication protocols like HTTP and APIs.
Data is transmitted using standard HTTP requests between the Flutter app and the Flask API.
Sensor data is collected in real-time and sent to the ThingSpeak platform for initial storage.
The data is then transmitted to the Flask API for processing and predictions.
The model is trained on a dataset that includes various health parameters such as temperature, heart rate, activity levels, and sleep patterns.
- Data Preprocessing: The dataset is cleaned, preprocessed (e.g., normalization, handling missing values), and split into training and testing sets.
- Model Selection: The RandomForestClassifier is chosen as the model due to its ability to handle non-linear relationships and provide feature importance insights.
- Hyperparameter Tuning: Hyperparameters (e.g., number of trees, tree depth) are tuned using techniques like grid search or cross-validation to optimize model performance.
- Model Training: The model is trained on the training data.
- Model Evaluation: The trained model is evaluated on the testing data using metrics such as accuracy, precision, recall, and F1-score.
The model's performance is evaluated using standard metrics to ensure accuracy.
The trained model is integrated into the Flask API for real-time predictions.
The API includes various endpoints:
/data: Receives sensor data from the ESP8266./predict: Provides health predictions based on input data./history: Retrieves historical health data for a given user.
The API loads the trained machine learning model and uses it to generate predictions based on the received sensor data.
Sensor data is preprocessed within the API to ensure it is in the correct format for the model (e.g., normalization, handling missing values).
The Flutter app features a user-friendly interface for monitoring health parameters.
- Real-time Monitoring: Displays live data from sensors (temperature, heart rate, activity).
- Health Predictions: Provides real-time health status updates based on API predictions.
- Historical Data Visualization: Displays historical health data in charts and graphs.
- Personalization: Allows users to set personalized health goals and receive notifications.
- User Profiles: Enables multiple users to create and manage their health data.
- Data Export: Allows users to export their health data for further analysis or sharing.
The app interacts with the Flask API via HTTP requests to:
- Send sensor data to the API.
- Receive health predictions from the API.
- Fetch historical health data for display.
- Start the ESP8266: Power on the ESP8266 and ensure it is connected to the Wi-Fi network.
- Start the Flask API: Run the Flask API server on the chosen hosting environment.
- Launch the Flutter App: Open the Flutter app on the user's device.
The API can be accessed using tools like Postman or cURL to send data and receive predictions.
The app fetches data from the API every 30 seconds and allows manual refreshes.
- Individual components (e.g., sensor drivers, API endpoints, model functions) are tested for correctness and functionality.
- Unit tests are written using appropriate testing frameworks (e.g., pytest for Python).
- End-to-end testing is conducted to ensure the complete system, from sensor data collection to data display in the Flutter app, functions as expected.
- Scenarios are tested to simulate real-world usage scenarios.
- The system is tested under various load conditions to assess its response time, scalability, and resource utilization.
- Tools like JMeter can be used to simulate multiple users and measure system performance.
The Flask API is deployed on Render for reliable hosting and scalability.
The deployment is designed to scale with increased user traffic by adding more instances of the API server on Render.
All communications between the ESP8266, the API, and the Flutter app are encrypted using HTTPS to protect data integrity and privacy.
User authentication and authorization mechanisms are implemented to protect user data and prevent unauthorized access.
Secure storage mechanisms are used to protect sensitive data (e.g., user credentials, health records).
- Monitor system performance and resource utilization.
- Regularly update dependencies (libraries, frameworks).
- Address any bugs or security vulnerabilities.
- Perform regular backups of data.
A comprehensive troubleshooting guide is provided to assist users in resolving common issues.
- GitHub repository: https://github.com/Kunal70616c/Body-Network-Area.git
- Libraries used: ESP8266 libraries, Flask, Flutter, etc.
- ESP8266: A low-cost Wi-Fi microcontroller.
- ThingSpeak: An IoT data platform for collecting and visualizing sensor data.
- Flask: A lightweight Python web framework for building APIs.
- Flutter: A cross-platform framework for building native mobile applications.
- RandomForestClassifier: A machine learning algorithm for classification.
- API: Application Programming Interface.
This detailed overview encapsulates the project's objectives, components, and functionalities, providing a comprehensive understanding of the Health Monitoring System.