A Lightweight Human Posture Predictive Assessment in Human-robot Collaboration via Diffusion Mamba

Accurate human posture assessment is essential for ensuring safety and ergonomics in human-robot collaboration. Traditional assessment methods rely on real-time human posture estimation, which, while effective, lacks predictive capabilities and diversity in motion outcomes, limiting its ability to assess ergonomic risks proactively. To address this, we propose MambaFusion, a lightweight and diverse human posture predictive assessment framework based on Diffusion Mamba. MambaFusion integrates diffusion models and Mamba to generate diverse future posture predictions, enhancing the flexibility of ergonomic evaluation. A CrossMamba block is introduced for noise prediction, where cross attention mechanisms improve contextual understanding by refining conditional embeddings, leading to more accurate motion representation. Additionally, a REBA-based ergonomic assessment module evaluates predicted human postures, enabling more comprehensive ergonomic risk assessments. Extensive experiments demonstrate that MambaFusion exhibits strong performance in prediction accuracy, motion diversity, and the reliability of ergonomic evaluation, and outperforms other algorithms.