Labanotation Generation Based on Bidirectional Gated Recurrent Units with Joint and Line Features

Abstract

Labanotation is an effective carrier for recording and displaying three-dimensional human movements. In the existing methods of Labanotation generation, the spatial characteristics are not fully considered. In addition, Long-term correlation of time series is not reflected. In this paper, we propose a novel method based on Bidirectional Gated Recurrent Units with Joint and Line features which can efficiently convert human movements into Labanotation. Firstly, Joint feature carries the location information; Line feature contains the direction information. These two types of features make full use of the correlation and co-occurrence between adjacent joints, thus embodying good spatial characteristics. Secondly, Bidirectional Gated Recurrent Units are applied to identify human movements. The unique gate-control structure of Bi-GRU can predict current status based on historical and future information. Thus, this method is good in timing modeling especially for long time series. The experimental results show that this method achieved an accuracy of 97.4%. It is higher than the state of the art, which demonstrating the effectiveness of our proposed method.