Abstract:
In today's workplace, mental health is increasingly seen as a critical component of employee well-being, with clear implications for productivity, job satisfaction, and overall organizational climate. Traditional means of monitoring employee mental health, like as surveys and personal evaluations, are sometimes subject to biases and mistakes, resulting in delayed or inefficient assistance for those in need. Our research provides a cutting-edge desktop application meant to monitor and support employee mental health through real-time emotion recognition, employing powerful CNN and ADT. ADT is used to improve the model's robustness against adversarial examples, ensuring system reliability even in the presence of potentially deceptive inputs. By training the deep learning model on a large dataset of facial expressions, the application can accurately recognize a wide range of emotions, including happiness, sadness, stress, and anger, providing significant insights into employees' mental well-being. Comprehensive testing and user acceptance studies have confirmed that the application is highly effective in detecting mental health disorders early, allowing for timely and suitable therapies. The application was tested using the current CNN model which showed an accuracy of 73% when the testing area was complex with many people moving around. However the application’s accuracy improved up to 86.67% when tested in a more favorable environment with 150 participants. It is shown that the application's emotion recognition skills are highly accurate and trustworthy due to advanced picture processing and data augmentation methods. This study offered a cutting-edge method for monitoring employee mental health through automated emotion recognition using deep learning algorithms such as CNN. The initiative, which was recognised as an important invention in this sector, created a sophisticated online system that efficiently analyses emotional expressions, paving the way for automated mental health monitoring.
