  Recent methods based on 3D skeleton data have achieved outstanding
performance due to its conciseness, robustness, and view-independent
representation. With the development of deep learning, Convolutional Neural
Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have
achieved promising performance for action recognition. However, for CNN-based
methods, it is inevitable to loss temporal information when a sequence is
encoded into images. In order to capture as much spatial-temporal information
as possible, LSTM and CNN are adopted to conduct effective recognition with
later score fusion. In addition, experimental results show that the score
fusion between CNN and LSTM performs better than that between LSTM and LSTM for
the same feature. Our method achieved state-of-the-art results on NTU RGB+D
datasets for 3D human action analysis. The proposed method achieved 87.40% in
terms of accuracy and ranked $1^{st}$ place in Large Scale 3D Human Activity
Analysis Challenge in Depth Videos.
