PoseRL-Net: human pose analysis for motion training guided by robot vision

Abstract

Objective: To address the limitations of traditional methods in human pose recognition, such as occlusions, lighting variations, and motion continuity, particularly in complex dynamic environments for seamless human-robot interaction. Method: We propose PoseRL-Net, a deep learning-based pose recognition model that enhances accuracy and robustness in human pose estimation. PoseRL-Net integrates multiple components, including a Spatial-Temporal Graph Convolutional Network (STGCN), attention mechanism, Gated Recurrent Unit (GRU) module, pose refinement, and symmetry constraints. The STGCN extracts spatial and temporal features, the attention mechanism focuses on key pose features, the GRU ensures temporal consistency, and the refinement and symmetry constraints improve structural plausibility and stability. Results: Extensive experiments conducted on the Human3.6M and MPI-INF-3DHP datasets demonstrate that PoseRL-Net outperforms existing state-of-the-art models on key metrics such as MPIPE and P-MPIPE, showcasing superior performance across various pose recognition tasks. Conclusion: PoseRL-Net not only improves pose estimation accuracy but also provides crucial support for intelligent decision-making and motion planning in robots operating in dynamic and complex scenarios, offering significant practical value for collaborative robotics.