An action identification method based on FSM and limb dry weight

This article mainly studies the motion recognition method used in the human-centered smart systems. First, we learn feature encoding sequences of the training sets, and then extract subactions from the learned sequences using a statistical model. Based on the hierarchical probabilistic context-free grammar characterization of limb sequences, we generate the grammatical rules of different actions according to the action training sets and characterize the actions and subactions using the finite state machine (FSM). To measure each limb sequence matching degree, we introduce a weight factor of the limb to perform gesture recognition. Also, we can get two sets of the compatibility of recognition probability when identifying the same limb movement sequence. Base on the endings, we derive the feature probability fusion formula of two sets of characteristics of limb movement sequence. Finally, we give the recognition results of two experiments and show the effect of the proposed method on several typical actions. The experimental results show that the proposed action recognition method applied on the same dataset has better recognition accuracy and less time cost over the other methods. © 2020, Springer-Verlag London Ltd., part of Springer Nature.