Abstract: With the widespread adoption of modern office environments, prolonged sitting has become the norm. However, existing posture recognition algorithms primarily rely on human pose estimation techniques, which suffer from feature loss due to occlusion and intra-class ambiguity in complex scenarios, limiting recognition accuracy. To enhance recognition performance, a Dual Perceptual Mixture of Attention Heads (DPMoA) module is proposed, which integrates the mixture of experts model with the multi-head attention mechanism. By introducing a noise term into the routing network, DPMoA dynamically activates different attention heads to improve generalization capability. Specifically, we design the Spatial Perception Interaction (SPI) and Content Perception Interaction (CPI) mechanisms to optimize global information capture and local occlusion handling, respectively. SPI dynamically activates key expert heads to facilitate feature extraction and global interaction, while CPI constructs a local feature model to enhance occlusion-aware perception. Experimental results demonstrate that DPMoA effectively improves recognition accuracy in complex scenarios while reducing model parameters.