Abstract: The Information Technology (IT) professionals often face high-stress levels due to the demanding nature of their work. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes. Developing robust methods for the rapid and accurate detection of human stress is of paramount importance. This research aims at developing a stress detection and management system specifically tailored for IT employees. Machine Learning algorithms and Deep Learning techniques like Convolutional Neural Networks are used for face emotion detection. Image Processing is used at the initial stage for detection. The existing methods for real time face emotion recognition and worker stress analysis has a draw-back that there is no live detection. Additionally, the existing system lacks a contextual data component to account for external factors influencing stress. The proposed system includes the real time live cam detection of Face emotion recognition and Worker Stress analysis and periodic analysis of employees and detecting mental stress levels through seamless integration of deep learning models for emotion detection. It provides a comprehensive understanding of employee well-being. The ultimate goal of our research is to identify emotion levels in employees, providing a foundation for stress management and thus enhance employee well-being and ultimately improving individual overall quality of life by enabling early stress detection.
Keywords: Image Processing; Convolutional Neural Networks; Stress
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DOI:
10.17148/IJARCCE.2024.13443
