HUMAN-DIGITAL TWIN SYNERGY FOR ADAPTIVE ERGONOMIC OPTIMIZATION USING NEUROERGONOMIC FEEDBACK AND AI-POWERED MOTION INTELLIGENCE IN SMART INDUSTRIAL WORKSPACES

In the era of Industry 5.0, the integration of digital twin technologies with human-centered design offers novel pathways to enhance ergonomic safety and efficiency in industrial environments. This paper proposes a synergistic framework that leverages neuroergonomic feedback and AI-driven motion intelligence to dynamically optimize worker posture, reduce musculoskeletal risk, and improve productivity in real time. By fusing biomechanical sensor data with digital twin simulations, the model adapts to individual differences and task complexities, enabling continuous ergonomic alignment. The research draws on neurophysiological feedback, motion capture analytics, and AI-based predictive modeling to build an adaptive system that personalizes ergonomic recommendations. Through an empirical simulation involving multiple ergonomic indicators, the approach demonstrates marked improvements in fatigue management, joint safety, and motion optimization. The findings underscore the potential of human-digital twin collaboration in transforming industrial ergonomics into an intelligent, responsive system.