This study proposes an emotion-aware RULA algorithm that combines facial emotion recognition with rapid upper limb assessment (RULA) to assess and promote ergonomic well-being in workplaces. The algorithm employs computer vision and machine learning to automatically detect and analyze facial emotions and upper limb postures. Integrating facial emotion recognition offers a unique advantage, revealing insights into stress-induced posture variations by considering the individual’s psychological state during ergonomic assessment. Real-time detection and analysis of facial emotions and upper limb postures enable prompt feedback, allowing individuals to make adjustments to their posture and emotional well-being. This evaluation system represents a promising approach to enhance workplace ergonomic assessment and support the overall well-being of workers. This approach stands out by being cost-effective and easy to set up, as it relies solely on two HD webcam and a personal computer without the need for extra sensors or equipment.