Accurate quantification of caged monkeys’ behaviors is a primary goal for the preclinical drug safety assessment. Skeleton information is important to the analysis on the behaviors of monkeys. However, most of the current skeleton-based action recognition methods usually extract the features of the skeleton sequence in the spatial and temporal dimensions, ignoring the integrity of the skeleton topology. To address this problem, we proposed a skeleton action recognition method based on the global spatiotemporal encode network (GSTEN). Based on the spatial temporal graph convolutional network (ST-GCN), the proposed method inserted global token generator (GTG) and several global spatiotemporal encoders (GSTE) in parallel to extract the global features in the spatiotemporal dimension. To verify the performance of the proposed method, we conducted experiments on a self-built monkey action recognition dataset. The experimental results show that the proposed GSTEN could achieve an accuracy of 76.54% without increasing the number of model parameters, which was 6.79% higher than the baseline model ST-CGN. 

action recognition, skeleton sequence, global spatiotemporal encode network, monkey, drug safety assessment