Movement trajectory recognition of sign language based on optimized dynamic time warping

Abstract

Movement trajectory recognition is the key link of sign language (SL) translation research, which directly affects the accuracy of SL translation results. A new method is proposed for the accurate recognition of movement trajectory. First, the gesture motion information collected should be converted into a fixed coordinate system by the coordinate transformation. The SL movement trajectory is reconstructed using the adaptive Simpson algorithm to maintain the originality and integrity of the trajectory. The algorithm is then extended to multidimensional time series by using Mahalanobis distance (MD). The activation function of generalized linear regression (GLR) is modified to optimize the dynamic time warping (DTW) algorithm, which ensures that the local shape characteristics are considered for the global amplitude characteristics and avoids the problem of abnormal matching in the process of trajectory recognition. Finally, the similarity measure method is used to calculate the distance between two warped trajectories, to judge whether they are classified to the same category. Experimental results show that this method is effective for the recognition of SL movement trajectory, and the accuracy of trajectory recognition is 86.25%. The difference ratio between the inter-class features and intra-class features of the movement trajectory is 20, and the generalization ability of the algorithm can be effectively improved.