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Augmented reality assisted assembly training (ARAAT) is an effective and affordable technique for labor training in the automobile and electronic industry. In general, most tasks of ARAAT are conducted by real-time hand operations. In this paper, we propose an algorithm of dynamic gesture recognition and prediction that aims to evaluate the standard and achievement of the hand operations for a given task in ARAAT. We consider that the given task can be decomposed into a series of hand operations and furthermore each hand operation into several continuous actions. Then, each action is related with a standard gesture based on the practical assembly task such that the standard and achievement of the actions included in the operations can be identified and predicted by the sequences of gestures instead of the performance throughout the whole task. Based on the practical industrial assembly, we specified five typical tasks, three typical operations, and six standard actions. We used Zernike moments combined histogram of oriented gradient and linear interpolation motion trajectories to represent 2D static and 3D dynamic features of standard gestures, respectively, and chose the directional pulse-coupled neural network as the classifier to recognize the gestures. In addition, we defined an action unit to reduce the dimensions of features and computational cost. During gesture recognition, we optimized the gesture boundaries iteratively by calculating the score probability density distribution to reduce interferences of invalid gestures and improve precision. The proposed algorithm was evaluated on four datasets and proved to increase recognition accuracy and reduce the computational cost from the experimental results.

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