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STRESS DETECTION IN IT PROFESSIONAL BY IMAGE PROCESSING AND MACHINE LEARNING: A Review

Tushar Patil Videet Jadhav
Indira College Of Engineering And Management Indira Collage Of Engineering And Management
tushar.patil@indiraicem.ac.in videet.jadhav@indiraicem.ac.in

Prof. Minal Jungare Prof. Rupali Adhau
Indira College Of Engineering And Management Indira College Of Engineering And Management
minal.jungare@indiraicem.ac.in rupali.adhau@indiraicem.ac.in

Abhishek Rajegaonkar Prajwal Thombare
Indira Collage Of Engineering And Management Indira Collage Of Engineering And Management
abhishek.rajegaonkar@indiraicem.ac.in prajwal.thombare@indiraicem.ac.in

Abstract

Stress is an inevitable part of the IT industry, where tight deadlines, long working hours, and high expectations often take a toll on employees’ mental and physical well-being. While traditional stress detection methods rely on self-reported surveys or physiological sensors, they often lack real-time monitoring and personalized support, making them less effective in a fast-paced work environment. Our project introduces an advanced stress detection system powered by Machine Learning (ML) and Image Processing, designed specifically for IT professionals.

Unlike older systems that focus only on survey-based assessments or physiological signals like heart rate, our solution takes a more comprehensive and real-time approach. It analyzes facial expressions, micro-expressions, and behavioral patterns using image processing techniques to detect signs of stress as they happen. In addition to live detection, our system conducts periodic assessments through short, intuitive surveys to track stress levels over time.

By combining real-time facial analysis with periodic psychological assessments, our system ensures a more holistic, proactive, and effective approach to stress management. This not only helps IT professionals maintain a healthier work-life balance but also creates a more positive, productive, and stress-free workplace. Our solution is a significant upgrade over traditional stress detection models, making workplaces smarter, healthier, and more supportive for employees.

Keywords

Stress detection, Computational intelligence, Observing the system in real-time, Stress signals, Stress check, Convolutional Neural Network (CNN), Support Vector Machines (SVM), Random Forest

Conclusion

This project represents a significant step forward in the integration of machine learning and image processing technologies to address workplace stress among IT professionals. By enabling real-time stress detection and providing personalized interventions, the system aims to foster a healthier and more productive work environment. Despite its potential, the project also faces several challenges, including data privacy concerns, accuracy of stress detection, and the ethical implications of continuous monitoring.

In the future, the system can be expanded and refined in several ways. Enhancements in data acquisition and processing could improve the accuracy of stress detection. Integrating additional physiological and behavioral indicators, such as heart rate variability and voice analysis, could provide a more comprehensive assessment of stress levels. Furthermore, incorporating personalized recommendations and adaptive feedback mechanisms could ensure that interventions are tailored to individual needs. By building on the current framework, future iterations of the project can contribute to more reliable, inclusive, and effective solutions for stress management in various workplace settings.

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