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Corrigible Action Recognition System through Motion-Sphere Trajectories for Standard Metaverse Actions

We propose a corrigible motion recognition system for metaverse applications, which can visualize motion data to analyze user motion and correct wrong motion data. To test the system performance, we created a metaverse dataset. The dataset consists of user in-terface/game/interaction classes, all of which are actions that can be used in metaverse applications. We collected motion data and conducted a recognition rate comparison experiment. We hypothe-size that the recognition rate could be enhanced using proper input data types. To verify this hypothesis, we converted the motion data into three data types and recognized the data using spatial-temporal graph convolutional networks. The experimental results revealed that the recognition rate varied according to the input data types. The highest recognition performance was derived from angle-based data types. Finally, we analyzed the reason for misclassifications using Motion-Sphere. During the analysis process, we determined the standard action of the training data. We corrected wrong action data according to the standard action and confirmed the increased recognition rate. We proved that our system could correct misclassification cases using standard action through the enhanced recognition rate.