This paper explores the integration of haptic gloves and virtual reality (VR) environments to enhance industrial training and operational efficiency within the framework of Industry 4.0 and Industry 5.0. It examines the alignment of these technologies with the Sustainable Development Goals (SDGs), mainly focusing on SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). By incorporating a human-centric approach, the study leverages haptic gloves to provide realistic feedback and immersive experiences in virtual training environments. The gloves enable intuitive interaction, enhancing the training efficacy and reducing real-world operational errors. Using the 5S principles—Social, Sustainable, Sensing, Smart, and Safe—this research evaluates the system’s impact across various dimensions. The findings indicate significant improvements in user comfort, productivity, and overall well-being, alongside enhanced sustainability and operational efficiency. However, challenges related to realistic hand-object interactions and algorithmic enhancements were identified. The study underscores the importance of continuous improvement and cross-disciplinary collaboration to advance the usability and effectiveness of these technologies. Future research should focus on customization, AI-driven adaptability, sustainability, real-world scalability, and comprehensive impact assessment to further develop smart interfaces in industrial settings. This integration represents a transformative opportunity to enhance workplace safety, skills development, and contribute to global sustainable development goals.

People are the very heart of our daily work and life. As we strive to leverage artificial intelligence to empower every person on the planet to achieve more, we need to understand people far better than we can today. Human–computer interaction plays a significant role in human-machine hybrid intelligence, and human understanding becomes a critical step in addressing the tremendous challenges of video understanding. In this paper, we share our views on why and how to use a human centric approach to address the challenging video understanding problems. We discuss human-centric vision tasks and their status, highlighting the challenges and how our understanding of human brain functions can be leveraged to effectively address some of the challenges. We show that semantic models, view-invariant models, and spatial-temporal visual attention mechanisms are important building blocks. We also discuss the future perspectives of video understanding.