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.
Highlights
Sign language (SL) is a complex dynamic mode accompanied by various gestures [1,2] and is the main form of communication for the deaf community
sign language (SL) translation mainly focuses on gesture recognition [5,6,7], whereas trajectory recognition is often simplified or ignored by many scholars
The generalized linear regression (GLR)-dynamic time warping (DTW) algorithm uses Mahalanobis distance (MD) to expand the dimension of time series and modifies the activation function of GLR to construct the regression coefficient of the MD matrix network, which can achieve the accurate matching of local shape characteristic points while taking into account the correlation of three-axis motion signals
Summary
Sign language (SL) is a complex dynamic mode accompanied by various gestures [1,2] and is the main form of communication for the deaf community. In Reference [12], ACC and EMG information are encoded to support vector machine (SVM) for gesture classification. In Reference [15], hidden Markov model (HMM) is used to recognize five kinds of dynamic gesture trajectories, and the average recognition rate is 84%. Five kinds of trajectories are difficult to meet the requirements of complex dynamic patterns of SL, and a recognition method with more types and higher accuracy is urgently needed. An effective measurement method for the similarity of movement sequences with different length is needed. Matching the key features representing the shape characteristics of time series, such as local peak value and valley value, is necessary to ensure the accuracy of similarity measurement between sequences. The results show that this method effectively recognizes SL movement trajectory
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