Stress Detection in IT Professionals Using Image Processing and Machine Learning

This project uses machine learning and image processing techniques to create a stress detection system for IT professionals. For instance, to identify stress indications like furrowed brows or tense facial muscles, the framework uses Convolutional Neural Networks (CNNs) to assess physiological signals and facial expressions from camera feeds. To acquire and assess data in real time, the system combines a Raspberry Pi with an attached camera module. A dataset obtained from IT professionals in a range of work environments has been employed to validate the proposed model's outstanding performance in identifying stress. This method of detection offers a non-invasive, affordably priced alternative to detect stress at work.