With the advent of Industry 5.0, the convergence of human intelligence and advanced automation is redefining industrial operations. Gesture recognition technology has emerged as a pivotal tool in this paradigm, facilitating seamless collaboration between human operators and intelligent systems. In this sense, the growing role of gesture recognition in Industry 5.0 emphasizes its potential to enhance productivity, safety, and efficiency on the factory floor. In this context, different artificial intelligence solutions have been adopted to perform gesture recognition. This thesis aims to compare different deep learning-based approaches to recognize operator’s posture (Human Pose Estimation) and track operator’s movements in view of preserving and guaranteeing workers’ well-being.