Recognition of human action is a daunting challenge considering action sequences'
embodied and dynamic existence. Recently designed material depth sensors and the …

Centrality measures in complex systems have been applied in serval domains for problem-
solving. In this paper, we tailor complex system measures to investigate the most interesting …

Human action analysis is an enthralling area of research in artificial intelligence, as it may
be used to improve a range of applications, including sports coaching, rehabilitation, and …

Critical applications ranging from sensitive military data to restricted area access demand
selective user authentication. The prevalent methods of tokens, passwords, and other …

With the development of depth sensors and pose estimation algorithms, human skeleton
action recognition based on graph convolutional networks has acquired widespread …

In recent years, convolutional neural networks (CNNs) have been used in many fields.
Nowadays, CNNs have a high learning capability, and this learning capability is …

To address the issue of high recognition error in conventional action error detection
methods, this article proposes a game of tennis serve error action detection algorithm based …

The current action recognition method has good effect when applied to static recognition,
but, when applied to dynamic action sequence recognition, the temporal and spatial feature …

Human pose estimation methods have difficulties predicting the correct pose for persons
due to challenges in scale variation. Existing works in this domain mainly focus on single …

Graph theory originated as a fun way to solve math problems, but it has now evolved into a
significant mathematics subject with several applications in computer vision. The human …