International Journal of Advances in Electrical Engineering
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P-ISSN: 2708-4574, E-ISSN: 2708-4582

International Journal of Advances in Electrical Engineering


2024, Vol. 5, Issue 2, Part A
Stress analysis and care prediction system using IoT


Author(s): Naveen Chillamcherla, Sneha Likitha Challala, Shiva Leela Bykani and Ravinder Oranganti

Abstract: During the COVID-19 pandemic, the shift to remote work has significantly heightened stress levels among online workers and students. This study delves into developing a predictive system utilizing the Internet of Things (IoT) to monitor and manage stress in real-time for individuals working from home. By analyzing physiological data collected through a Galvanic Skin Response (GSR) sensor, the system identifies stress levels and suggests personalized interventions like meditation, yoga, music, or exercise. Our desktop application, based on this model, demonstrates over 70% accuracy in stress prediction, offering substantial benefits for improving the health and well-being of remote workers. Future enhancements will be guided by user feedback, aiming to refine the system's effectiveness and utility. This research not only underscores the critical need for proactive stress management solutions for online workers but also showcases the potential of IoT technologies in addressing these emerging health challenges.

Pages: 01-05 | Views: 500 | Downloads: 265

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International Journal of Advances in Electrical Engineering
How to cite this article:
Naveen Chillamcherla, Sneha Likitha Challala, Shiva Leela Bykani, Ravinder Oranganti. Stress analysis and care prediction system using IoT. Int J Adv Electr Eng 2024;5(2):01-05.
International Journal of Advances in Electrical Engineering
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