Generative AI-Driven High-Fidelity Human Motion Simulation

Human motion simulation (HMS) supports cost-effective evaluation of worker behavior, safety, and productivity in industrial tasks. However, existing methods often suffer from low motion fidelity. This study introduces Generative-AI-Enabled HMS (G-AI-HMS), which integrates text-to-text and text-to-motion models to enhance simulation quality for physical tasks. G-AI-HMS tackles two key challenges: (1) translating task descriptions into motion-aware language using Large Language Models aligned with MotionGPT's training vocabulary, and (2) validating AI-enhanced motions against real human movements using computer vision. Posture estimation algorithms are applied to real-time videos to extract joint landmarks, and motion similarity metrics are used to compare them with AI-enhanced sequences. In a case study involving eight tasks, the AI-enhanced motions showed lower error than human created descriptions in most scenarios, performing better in six tasks based on spatial accuracy, four tasks based on alignment after pose normalization, and seven tasks based on overall temporal similarity. Statistical analysis showed that AI-enhanced prompts significantly (p $<$ 0.0001) reduced joint error and temporal misalignment while retaining comparable posture accuracy.

Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantification

The performance of physical workers is significantly influenced by the quantity of their motions. However, monitoring and assessing these motions is challenging due to the complexities of motion sensing, tracking, and quantification. Recent advancements have utilized in-situ video analysis for real-time observation of worker behaviors, enabling data-driven quantification of motion amounts. Nevertheless, there are limitations to monitoring worker movements using video data. This paper introduces a novel framework based on computer vision to track and quantify the motion of workers' upper and lower limbs, issuing alerts when the motion reaches critical thresholds. Using joint position data from posture estimation, the framework employs Hotelling's T$^2$ statistic to quantify and monitor motion amounts, integrating computer vision tools to address challenges in automated worker training and enhance exploratory research in this field. We collected data of participants performing lifting and moving tasks with large boxes and small wooden cubes, to simulate macro and micro assembly tasks respectively. It was found that the correlation between workers' joint motion amount and the Hotelling's T$^2$ statistic was approximately 35% greater for micro tasks compared to macro tasks, highlighting the framework's ability to identify fine-grained motion differences. This study demonstrates the effectiveness of the proposed system in real-time applications across various industry settings. It provides a tool for enhancing worker safety and productivity through precision motion analysis and proactive ergonomic adjustments.