Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …

Skeleton-based human action recognition has caused wide concern, as skeleton data can
robustly adapt to dynamic circumstances such as camera view changes and background …

To facilitate data-driven and informed decision making, a novel deep neural network
architecture for human activity recognition based on multiple sensor data is proposed in this …

Although skeleton-based action recognition has achieved great success in recent years,
most of the existing methods may suffer from a large model size and slow execution speed …

Skeleton-based action recognition has been extensively studied, but it remains an unsolved
problem because of the complex variations of skeleton joints in 3-D spatiotemporal space …

In vision-based action recognition, spatio-temporal features from different modalities are
used for recognizing activities. Temporal modeling is a long challenge of action recognition …

In recent years, graph convolutional networks (GCNs) play an increasingly critical role in
skeleton-based human action recognition. However, most GCN-based methods still have …

Neighboring frames are more correlated compared to frames from further temporal
distances. In this paper, we aim to explore the temporal correlations among neighboring …

To fluently collaborate with people, robots need the ability to recognize human activities
accurately. Although modern robots are equipped with various sensors, robust human …