The current human pose estimation networks are difficult to be widely used in mobile devices and embedded platforms due to the arithmetic power and memory limitations. To address this problem， this paper proposes a lightweight human pose estimation network X-HRNet with HRNet as the basic framework and uses the ResNeXt module to replace the common basic module to reduce the parameters and computational complexity of the network. The proposed model achieves 78.2% accuracy on the COCO validation set， which is 1.9% higher than that of the HRNet， the number of parameters decreases by 22.2M， and the computational effort decreases by 27.3 GFLOPs. The proposed X-HRNet is a method with the combination accuracy and lightweight， which proposes a new lightweight human pose estimation network for embedded platforms by reducing the computation and the number of parameters effectively while maintaining accuracy.