Leveraging Transfer Learning to Enhance Generative Postural Variability for Ergonomics

Generative AI holds promise for advancing human factors and ergonomics. However, limited training data can reduce variability in generative models. This study investigates using transfer learning to enhance variability in generative posture prediction. We pre-trained a conditional diffusion model on lifting postures where hands are near body center and fine-tuned it on limited extended-reach postures. Compared to training from scratch, transfer learning significantly improved joint angle variability across multiple body segments while maintaining similar accuracy in posture similarity and validity. Additionally, it reduced training time by over 90%, demonstrating efficiency benefits. These findings highlight transfer learning’s potential to enrich generative model outputs with more variable ergonomics data, supporting scalable and adaptive workplace design.