We present a sensor-fusion method that exploits a depth camera and a gyroscope to track the articulation of a hand in the presence of excessive motion blur. In case of slow and smooth hand motions, the existing methods estimate the hand pose fairly accurately and robustly, despite challenges due to the high dimensionality of the problem, self-occlusions, uniform appearance of hand parts, etc. However, the accuracy of hand pose estimation drops considerably for fast-moving hands because the depth image is severely distorted due to motion blur. Moreover, when hands move fast, the actual hand pose is far from the one estimated in the previous frame, therefore the assumption of temporal continuity on which tracking methods rely, is not valid. In this paper, we track fast-moving hands with the combination of a gyroscope and a depth camera. As a first step, we calibrate a depth camera and a gyroscope attached to a hand so as to identify their time and pose offsets. Following that, we fuse the rotation information of the calibrated gyroscope with model-based hierarchical particle filter tracking. A series of quantitative and qualitative experiments demonstrate that the proposed method performs more accurately and robustly in the presence of motion blur, when compared to state of the art algorithms, especially in the case of very fast hand rotations.