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HVS-4687. Machine Learning-Based Stress Analysis and Care Prediction Using IoT
HVS-4687. Machine Learning-Based Stress Analysis and Care Prediction Using IoT
HVS-4687. Machine Learning-Based Stress Analysis and Care Prediction Using IoT

HVS-4687. Machine Learning-Based Stress Analysis and Care Prediction Using IoT

₹14,500.00

This project presents an IoT-based Stress Analysis and Care Prediction System designed to monitor and manage stress levels in real time.

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In the rapidly evolving digital workspace, online workers often experience high levels of stress due to long working hours, reduced physical interaction, and blurred work-life boundaries. This project presents an IoT-based Stress Analysis and Care Prediction System designed to monitor and manage stress levels in real time. The system uses an ESP32 microcontroller to collect physiological data from a heartbeat sensor, temperature sensor, and GSR (Galvanic Skin Response) sensor, which serve as key indicators of stress. The gathered data are transmitted to a Raspberry Pi Zero 2W, which performs machine learning–based analysis to identify stress patterns and predict mental health risks. The analyzed data are also uploaded to the ThingSpeak cloud platform for remote monitoring, data visualization, and long-term trend analysis. Based on the predictions, the system provides personalized care suggestions such as rest reminders, breathing exercises, and hydration alerts. An LCD display presents real-time readings and feedback to the user. By integrating IoT sensing, cloud computing, and intelligent data processing, this system promotes mental well-being, enhances productivity, and supports preventive healthcare among online workers. The proposed solution holds strong potential for deployment in remote work environments and corporate wellness programs.    

The main objectives of the project are:
  • To monitor the stress levels of online workers in real time using IoT sensors.
  • To collect physiological data such as heart rate, skin conductivity, and body temperature.
  • To analyze the collected data using machine learning on Raspberry Pi Zero 2W.
  • To transmit sensor data to the ThingSpeak cloud platform for remote monitoring and data visualization.
  • To predict stress conditions and provide suitable care recommendations.
  • To display real-time readings and feedback on an LCD screen.
  • To promote mental wellness and preventive healthcare among online workers.
    The major building blocks of this project are:
  1. Power Supply
  2. Raspberry pi Zero 2w.
  3. ESP32.
  4. Heartbeat sensor.
  5. Temperature sensor.
  6. GSR sensor.
  7. LCD display.
  8. Buzzer.
  9. SD card.
  Software’s used:  
  1. Python Language.
  2. Python IDE software
  3. Express SCH for Circuit design.
       

video:

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