Towards AI-Driven Digital Twins for Real-Time Optimization of Remote Workspaces for Safety, Comfort, and Ergonomics

We present an early-stage framework for the intelligent evaluation and optimization of remote workspaces using Artificial Intelligence (AI), with a vision toward future integration into Digital Twins and Web3D environments. The proposed system leverages computer vision techniques to identify ergonomic, safety, and comfort-related issues from workspace images, and suggests improvements through an interactive checklist interface. Current components include image-based hazard detection using object recognition models (e.g., YOLO), depth and segmentation models, and a rule-based recommendation engine. We also explore the potential integration of ergonomic assessments through pose estimation (e.g., BlazePose) and environmental parameters such as CO2, light, and noise via smart sensors for future development of a Digital Twin interface. A preliminary case study involving a home office and a coworking space demonstrates the effectiveness of the visual analysis pipeline and recommendation features. While real-Time sensor integration and full 3D workspace reconstruction remain future work, our results suggest that AI-based workspace analysis can serve as a foundation for scalable, user-centric ergonomic assessment platforms.