Stress Monitoring with Computer Vision and Machine Learning for Software Employees

In today's fast-paced work environments, stress management is paramount for employee well-being and productivity. This study proposes a novel approach to stress monitoring utilizing computer vision and machine learning techniques tailored for software employees. It involves the collection of multimodal data during work hours, including facial images and physiological signals, which are then processed using computer vision algorithms and machine learning models. Facial expression analysis enables real-time detection of stress indicators, while physiological signals provide additional context for stress assessment. The implementation of this system offers several benefits, including early detection of stress patterns, personalized stress management interventions, and insights into workplace stressors. Ultimately, our goal is to enhance employee well-being, productivity, and job satisfaction in the software industry through proactive stress monitoring and intervention strategies.