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HVS-4660. Fatigue and Mental stress Monitoring with EEG and GSR sensors using Raspberry Pi
HVS-4660. Fatigue and Mental stress Monitoring with EEG and GSR sensors using Raspberry Pi
HVS-4660. Fatigue and Mental stress Monitoring with EEG and GSR sensors using Raspberry Pi
HVS-4660. Fatigue and Mental stress Monitoring with EEG and GSR sensors using Raspberry Pi
HVS-4660. Fatigue and Mental stress Monitoring with EEG and GSR sensors using Raspberry Pi
HVS-4660. Fatigue and Mental stress Monitoring with EEG and GSR sensors using Raspberry Pi

HVS-4660. Fatigue and Mental stress Monitoring with EEG and GSR sensors using Raspberry Pi

₹21,000.00

This project presents the design and implementation of a Wearable Fatigue and Mental Stress Monitoring Head Cap for real-time assessment and management of mental stress. The system employs EEG and GSR sensors to monitor brain activity

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This project presents the design and implementation of a Wearable Fatigue and Mental Stress Monitoring Head Cap for real-time assessment and management of mental stress. The system employs EEG and GSR sensors to monitor brain activity and physiological stress variations, which are strong indicators of mental fatigue. The sensor data is acquired and conditioned by an Arduino Nano and transmitted to a Raspberry Pi Zero 2W for advanced analysis. The Raspberry Pi processes the incoming data to calculate a stress score ranging from 0 to 100, representing the user’s current mental stress level. Based on this score, intelligent decision logic determines the system response. When the stress level exceeds a predefined threshold, the Raspberry Pi automatically activates a relay to drive an integrated massage unit, providing immediate physical relaxation. In addition to automatic control, the system also offers a user-controlled massager through a web-based interface, allowing the user to manually turn the massager ON or OFF at any time without affecting EEG and GSR signal acquisition. The Raspberry Pi Zero 2W generates alert messages and notifications during high-stress conditions and logs all sensor data and stress scores on an SD card for future analysis. A dedicated web page displays real-time stress levels, historical trends, and personalized AI-based recommendations such as relaxation exercises and breathing techniques. Powered by a Li-ion battery, the proposed wearable head cap is compact, portable, and energy efficient. The system provides a comprehensive solution for mental fatigue monitoring, stress reduction, and user-centric control, making it suitable for healthcare monitoring, workplace wellness, and safety-critical applications.

The major Objectives of this project are:  
  • To acquire EEG and GSR signals related to mental stress
  • To process sensor data and compute a stress score (0–100)
  • To automatically activate a massage unit during high stress
  • To provide a web-based interface for real-time monitoring and control
  • To log stress data for future analysis and trends
  • To generate AI-based stress relief recommendations
    The major building blocks of this project are:  
  1. Li-ion Battery Power Supply.
  2. Raspberry pi Zero 2W.
  3. SD Card.
  4. EEG amplifier bio amp EXG pill.
  5. GSR.
  6. Relay along with Massage.
  7. Arduino nano.
  8. CAP.
  Software’s used:  
  • Python programming.
  • Raspbian OS.
  1. Express SCH for Circuit design.
  • WEB technology.
   

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