Hand gesture recognition plays a crucial role in the domain of computer vision, as it enhances human-computer interaction by enabling intuitive, touch-free control and communication. While offline methods have made significant advances in isolated gesture recognition, real-world applications demand online and continuous processing. Skeleton-based methods, though effective, face challenges due to the intricate nature of hand joints and the diverse 3D motions they induce. This paper introduces AG-MAE, a novel approach that integrates anatomical constraints to guide the self-supervised training of a spatio-temporal masked autoencoder, enhancing the learning of 3D keypoint representations. By incorporating anatomical knowledge, AG-MAE learns more discriminative features for hand poses and movements, subsequently improving online gesture recognition. Evaluation on standard datasets demonstrates the superiority of our approach and its potential for real-world applications