A method of automatically generating Labanotation from human motion capture data

This paper presents a method of automatically generating Labanotation scores from human motion capture data. Up to now the main acquisition of Labanotation is manual recording by the professionals. Our work allows the users converting human motions to Labanotation scores efficiently. The key components of our method are the analysis of motion capture data, the segmentation of motion and the recognition of each motion fragment. In motion segmentation, we make the results aligned with the beat of Labanotation to ensure the generated symbols regular and accurate. In movement recognition, according to the different properties of human motion, we deal with the data in different suitable ways. Therefore, our recognition results are more reliable than previous works. The experiments show that our work is a useful tool for converting human dance motions into Labanotation scores. Further, considering its efficiency the method can be used to record large numbers of ethnic dances that coming to the crisis of being lost.