Personalized Health Monitoring for Mental Well-being

🏥Overview

The Personalized Health Monitoring for Mental Well-being project is an IoT-enabled system designed to monitor both physiological and environmental factors. By analyzing heart rate, SpO2, skin conductivity, and ambient temperature, it provides real-time insights into stress levels, offers personalized wellness recommendations, and facilitates connections to mental health professionals in emergencies.

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✨Features

• Real-Time Monitoring: Collects and processes data from multiple sensors.
• Stress Analysis: Combines heart rate and GSR data to estimate stress levels.
• Personalized Recommendations: Offers dynamic wellness tips to users.
• Data Visualization: Interactive graphs powered by Kivy for real-time insights.
• Emergency Support: Connects users to mental health resources.

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🛠️Architecture

Key Components:

• ESP32 Microcontroller: Collects and transmits sensor data.
• MAX30102: Monitors heart rate and blood oxygen levels (SpO2).
• GSR Sensor: Measures skin conductivity to assess stress.
• DS18B20: Records ambient temperature.
• ThingSpeak Integration: Cloud storage for real-time data.
• Kivy App: Provides a sleek, user-friendly interface.

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📁Project Structure

personalized-health-monitoring/
├── backend.cpp               # ESP32 Backend Code
├── frontend.py               # Kivy-Based Frontend
├── LICENSE                   # License Information
└── README.md                 # Project Documentation

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🚀Prerequisites

• Hardware: ESP32, MAX30102, GSR sensor, DS18B20
• Software: Arduino IDE, Python 3.x, Kivy framework
• Libraries:
  • ThingSpeak for cloud integration
  • requests for API calls
  • kivy for UI development

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📦Installation

1. Clone the Repository:

git clone https://github.com/SwayamKohli/Personalized-Health-Monitoring-for-Mental-Well-being.git
cd Personalized-Health-Monitoring-for-Mental-Well-being

2. Set Up the Backend:

• Upload backend.cpp to your ESP32 device.
• Configure Wi-Fi credentials in the code.

3. Set Up the Frontend: Ensure Python 3.x and the Kivy framework are installed:

pip install kivy requests

4. Run the Application:

python frontend.py

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🎯Usage

1. Collect Data: Ensure the sensors are connected and transmitting data.
2. Monitor Health: Visualize real-time metrics in the Kivy app.
3. Emergency Contact: Use the app to access mental health support.

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🌟Future Improvements

• Machine learning models for advanced stress prediction.
• Mobile app integration for remote monitoring.
• Multi-device synchronization for comprehensive health tracking.

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🐈‍⬛ GitHub Profiles of Creators:

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⚖️ License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International Public License.
For the full terms of this license, visit Creative Commons Legal Code.

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