To create a Virtual Reality (VR) system for Wing Chun’s basic hand movement training, capturing, and classifying movement data is an important step. The main goal of this paper is to find the best possible method of classifying hand movement, particularly Wing Chun’s basic hand movements, to be used in the VR training system. This paper uses Oculus Quest VR gear and Unreal Engine 4 to capture features of the movement such as location, rotation, angular acceleration, linear acceleration, angular velocity, and linear velocity. RapidMiner Studio is used to pre-process the captured data, apply algorithms, and optimize the generated model. Algorithms such as Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (kNN) are applied, optimized, and compared. By classifying 10 movements, the result shows that the optimized kNN algorithm obtained the highest averaged performance indicators: Accuracy of 99.94%, precision of 99.70%, recall of 99.70%, and specificity of 99.97%. The overall accuracy of the optimized kNN is 99.71%.
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