  This paper contributes to the challenge of skeleton-based human action
recognition in videos. The key step is to develop a generic network
architecture to extract discriminative features for the spatio-temporal
skeleton data. In this paper, we propose a novel module, namely Logsig-RNN,
which is the combination of the log-signature layer and recurrent type neural
networks (RNNs). The former one comes from the mathematically principled
technology of signatures and log-signatures as representations for streamed
data, which can manage high sample rate streams, non-uniform sampling and time
series of variable length. It serves as an enhancement of the recurrent layer,
which can be conveniently plugged into neural networks. Besides we propose two
path transformation layers to significantly reduce path dimension while
retaining the essential information fed into the Logsig-RNN module. Finally,
numerical results demonstrate that replacing the RNN module by the Logsig-RNN
module in SOTA networks consistently improves the performance on both Chalearn
gesture data and NTU RGB+D 120 action data in terms of accuracy and robustness.
In particular, we achieve the state-of-the-art accuracy on Chalearn2013 gesture
data by combining simple path transformation layers with the Logsig-RNN. Codes
are available at https://github.com/steveliao93/GCN_LogsigRNN.
