Graph convolutional networks have been widely used for skeleton-based action recognition
due to their excellent modeling ability of non-Euclidean data. As the graph convolution is a …

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 …

Ambient Assisted Living is a concept that focuses on using technology to support and
enhance the quality of life and well-being of frail or elderly individuals in both indoor and …

Skeleton-based gesture recognition is very challenging, as the high-level information in
gesture is expressed by a sequence of complexly composite motions. Previous works often …

Recently, human activity recognition using skeleton data is increasing due to its ease of
acquisition and finer shape details. Still, it suffers from a wide range of intra-class variation …

Hand gesture recognition (HGR) plays a significant role in interpreting the meaning of sign
language, human–computer interaction, and robot control. This paper proposes a real-time …

In-process monitoring (IPM) of laser deep penetration welding (LDPW) has witnessed a
rapid growth in approaches that embrace machine learning algorithms, utilizing raw sensor …

With the development of 3D hand pose estimation technologies, skeleton-based dynamic
hand gesture recognition has attracted widespread attention. In this paper, we propose a …

GCN-based methods have achieved remarkable performance in skeleton-based action
recognition. However, existing methods have not explicitly attempted to remove temporal …

Previous methods for skeleton-based gesture recognition mostly arrange the skeleton
sequence into a pseudo image or spatial–temporal graph and apply a deep Convolutional …