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A wearable stress monitoring system, developed with Arduino and sensors (heart rate, skin conductivity, temperature), provides real-time insights into stress levels. Data is visualized on OLED and transmitted to the Blynk app for comprehensive analysis and personalized health management.

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Ashprogrammer29/Stress-Monitoring-Device

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⌚ Wearable Stress Monitoring Device

This project documents the development of a wearable device designed to monitor stress levels in real-time. The device utilizes a combination of sensors to track key physiological indicators, including heart rate, skin conductivity (GSR), and body temperature.

Key Features:

  • Real-time Stress Monitoring: Continuously tracks heart rate, skin conductivity, and body temperature.
  • Wireless Data Transmission: Transmits data wirelessly to a Telegram bot for remote monitoring and analysis.
  • Compact and Wearable: 3D-printed casing ensures a comfortable and portable design.
  • User-Friendly: OLED display provides immediate feedback on stress levels.
  • Rechargeable Battery: Powered by a rechargeable Li-ion battery for extended usage.

Components:

  • Arduino R4 Wi-Fi: Microcontroller for data processing and communication.
  • MAX30105 Heart Rate Sensor: Measures heart rate using photoplethysmography.
  • BME280 Temperature Sensor: Measures ambient temperature.
  • GSR Sensor: Measures skin conductivity.
  • OLED Display: Displays real-time data.
  • Li-ion Battery: Provides power to the device.
  • 3D-Printed Casing: Houses all components.
  • Telegram Bot: Enables remote data monitoring.

Hardware Setup:

  1. Sensor Integration:
    • Connect the MAX30105, BME280, and GSR sensors to the Arduino R4 Wi-Fi board using I2C communication.
    • Connect the OLED display to the Arduino for data visualization.
  2. Power Supply:
    • Connect the Li-ion battery to the Arduino and integrate the charging circuit (TP4056).
  3. Casing Assembly:
    • Assemble the 3D-printed casing and securely mount all components.

Software Development:

  1. Arduino Code:
    • Write Arduino code to:
      • Read sensor data from the MAX30105, BME280, and GSR sensors.
      • Process sensor data and calculate stress indices (optional).
      • Display real-time data on the OLED screen.
      • Transmit data wirelessly to the Telegram bot using the UniversalTelegramBot library.
  2. Telegram Bot Setup:
    • Create a Telegram bot using BotFather.
    • Obtain the bot's API key and your Telegram chat ID.
    • Integrate the API key and chat ID into the Arduino code.

Usage:

  1. Charge the device: Use the provided charging circuit.
  2. Wear the device: Position the device comfortably on your wrist.
  3. Monitor data: View real-time stress data on the OLED display and receive updates on your Telegram account.

Future Work:

  • Machine Learning Integration: Implement machine learning algorithms to predict stress levels more accurately.
  • Data Visualization: Develop a more sophisticated dashboard for data visualization and analysis.
  • User Interface Enhancements: Improve the user interface for easier interaction with the device.
  • Stress Management Features: Integrate stress-reducing exercises or mindfulness techniques into the device.

🤝 Contributions

  • Aswin Deivanayagam S: GitHub
  • Kishore Muruganantham: GitHub

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A wearable stress monitoring system, developed with Arduino and sensors (heart rate, skin conductivity, temperature), provides real-time insights into stress levels. Data is visualized on OLED and transmitted to the Blynk app for comprehensive analysis and personalized health management.

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