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 …

Human action recognition systems use data collected from a wide range of sensors to
accurately identify and interpret human actions. One of the most challenging issues for …

Spatial-temporal graphs have been widely used by skeleton-based action recognition
algorithms to model human action dynamics. To capture robust movement patterns from …

Despite the great progress in 3D human pose estimation from videos, it is still an open
problem to take full advantage of a redundant 2D pose sequence to learn representative …

Skeleton-based gesture recognition methods have achieved high success using Graph
Convolutional Network (GCN), which commonly uses an adjacency matrix to model the …

This work focuses on the task of elderly activity recognition, which is a challenging task due
to the existence of individual actions and human-object interactions in elderly activities …

Skeleton-based action recognition is an important task that requires the adequate
understanding of movement characteristics of a human action from the given skeleton …

The dynamic hand skeleton data have become increasingly attractive to widely studied for
the recognition of hand gestures that contain 3D coordinates of hand joints. Many …

Contrastive learning has been successfully leveraged to learn action representations for
addressing the problem of semisupervised skeleton-based action recognition. However …

Automated human posture estimation (A-HPE) systems need delicate methods for detecting
body parts and selecting cues based on marker-less sensors to effectively recognize …