Novel Algorithm for Hand Gesture Recognition Utilizing a Wrist-Worn Inertial Sensor

In this paper, we propose a dynamic time warping (DTW)-based template matching method with a novel template generation algorithm for hand gesture recognition utilizing a wrist-worn inertial sensor. Hand orientation measurements are utilized to reconstruct gesture trajectories. The DTW technique with the Riemannian distance is employed to perform similarity measurements between gesture orientation trajectories. The proposed gesture recognition algorithm comprises three stages: a data preprocessing stage, a training stage, and a recognition stage. First, a moving intrinsic average filter is introduced to suppress the effects of measurement noise and unconscious hand shaking. Next, a double-threshold segmentation scheme is applied to extract individual gesture segments. In the training stage, to cope with temporal and spatial variations in gestures, an adaptive DTW barycenter averaging algorithm combined with an intrinsic averaging method is developed to generate gesture templates. A suitable rejection threshold is determined according to intra-class DTW distances. In the recognition stage, the rejection percentage between the input gesture and each gesture template is calculated. Finally, the nearest neighbor decision rule is applied to determine the recognition result. Experiments performed on a database of 3600 gesture samples illustrate that the proposed DTW-based gesture template generation and classification algorithm outperforms existing methods.