High-Fidelity Worker Motion Simulation With Generative AI

Abstract

Worker motion simulation synthesizes human movements in specific work scenarios to analyze behavior and assess performance, offering a cost- effective way to evaluate safety and productivity. However, existing studies struggle with high fidelity and precision. Recent advances in Generative AI enhance motion simulation. This study integrates ChatGPT and MotionGPT AI models to generate high-fidelity motions for tasks like lifting objects with the non-dominant hand or walking five steps to the right. To improve accuracy, ChatGPT-generated guidance was aligned with MotionGPT’s training vocabulary. By analyzing the HumanML3D dataset, a JSON file of word frequencies was created to adjust input prompts to match the training data’s patterns. This strategy mitigates out-of-distribution issues, refining MotionGPT’s accuracy. Simulated motions were validated against real human motions using computer vision-based video analysis. By comparing body landmarks, we quantitatively improved fidelity. This study advances AI-aided worker motion simulation and provides a new method for AI performance evaluation in industrial settings.