Yuxin Gao¹, Xiaodong Duan^2,3, Qiguo Dai^2,3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 701-718, 2025, DOI:10.32604/cmc.2025.060137 - 26 March 2025

Abstract Graph convolutional network (GCN) as an essential tool in human action recognition tasks have achieved excellent performance in previous studies. However, most current skeleton-based action recognition using GCN methods use a shared topology, which cannot flexibly adapt to the diverse correlations between joints under different motion features. The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms. In this work, we propose a novel graph convolutional learning framework, called PCCTR-GCN, which integrates pose correction and channel topology refinement for skeleton-based human action… More >

Ange Chen, Chengdong Wu^*, Chuanjiang Leng

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 173-191, 2025, DOI:10.32604/cmc.2024.059284 - 03 January 2025

Abstract Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly, meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused. Moreover, existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs, making the correlation weights between nodes in the graph and their neighborhood nodes shared. Existing Graph Convolutional Networks (GCNs) cannot extract global and deep-level skeleton structure information and view… More >

Yuxiang Zou¹, Ning He^2,*, Jiwu Sun¹, Xunrui Huang¹, Wenhua Wang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1255-1276, 2025, DOI:10.32604/cmc.2024.055732 - 03 January 2025

Abstract In recent years, gait-based emotion recognition has been widely applied in the field of computer vision. However, existing gait emotion recognition methods typically rely on complete human skeleton data, and their accuracy significantly declines when the data is occluded. To enhance the accuracy of gait emotion recognition under occlusion, this paper proposes a Multi-scale Suppression Graph Convolutional Network (MS-GCN). The MS-GCN consists of three main components: Joint Interpolation Module (JI Moudle), Multi-scale Temporal Convolution Network (MS-TCN), and Suppression Graph Convolutional Network (SGCN). The JI Module completes the spatially occluded skeletal joints using the (K-Nearest Neighbors)… More >

Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

Ting Tan^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012670

Abstract Owing to their compliance, soft robots demonstrate enhanced compatibility with humans and the environment compared with traditional rigid robots. However, ensuring the working effectiveness of artificial muscles that actuate soft robots in confined spaces or underloaded conditions remains a challenge. Drawing inspiration from avian pneumatic bones, we propose the incorporation of a light weight endoskeleton into artificial muscles to augment the mechanical integrity and tackle load-bearing environmental difficulties. We present a soft origami hybrid artificial muscle that features a hollow origami metamaterial interior with a rolled dielectric elastomer exterior. The programmable nonlinear origami metamaterial endoskeleton More >

Vesal Khean¹, Chomyong Kim², Sunjoo Ryu², Awais Khan¹, Min Kyung Hong³, Eun Young Kim⁴, Joungmin Kim⁵, Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 773-787, 2024, DOI:10.32604/cmc.2024.056767 - 15 October 2024

Abstract Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such as hugging, kicking, pushing, pointing, and no interaction, from video sequences using multiple cameras. In this study, a hybrid Deep… More >

Huiyan Liang¹, Wei Fang¹, Xiqiao Feng^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012719

Abstract Cell–extracellular matrix (ECM) interactions play a pivotal role in many functions of cells, for example, sensing, signaling, migration, and gene expression. The spatial-temporal dynamic evolution of cell–substrate adhesions involves complicated mechano-bio-chemical coupling mechanisms of integrin, adaptor and signaling proteins, and the interplay between the cytoskeleton and ECM as well. In this paper, we establish a multiscale dynamic model of cell–substrate interfaces considering intermolecular force transmission pathways, i.e., intra- and extra-cellular bond dynamics, and mechanochemical coupling regulations. To illustrate its applications, this model is used to reproduce several adhesion-related experimental phenomena of cells, including substrate rigidity… More >

Yunhe Wang¹, Yuxin Xia², Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4489-4507, 2024, DOI:10.32604/cmc.2024.048813 - 26 March 2024

Abstract In recent years, skeleton-based action recognition has made great achievements in Computer Vision. A graph convolutional network (GCN) is effective for action recognition, modelling the human skeleton as a spatio-temporal graph. Most GCNs define the graph topology by physical relations of the human joints. However, this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs, resulting in a low recognition rate for specific actions with implicit correlation between joint pairs. In addition, existing methods ignore the trend correlation between adjacent frames within an action… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Abu Saleh Musa Miah¹, Kota Suzuki¹, Koki Hirooka¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2605-2625, 2024, DOI:10.32604/cmes.2023.046334 - 11 March 2024

Abstract Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities. In Japan, approximately 360,000 individuals with hearing and speech disabilities rely on Japanese Sign Language (JSL) for communication. However, existing JSL recognition systems have faced significant performance limitations due to inherent complexities. In response to these challenges, we present a novel JSL recognition system that employs a strategic fusion approach, combining joint skeleton-based handcrafted features and pixel-based deep learning features. Our system incorporates two distinct streams: the first stream extracts crucial handcrafted features, emphasizing the capture of hand and body… More >

Rosepreet Kaur Bhogal^1,*, V. Devendran²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2759-2772, 2023, DOI:10.32604/csse.2023.034862 - 09 November 2023

Abstract Human activity recognition is a recent area of research for researchers. Activity recognition has many applications in smart homes to observe and track toddlers or oldsters for their safety, monitor indoor and outdoor activities, develop Tele immersion systems, or detect abnormal activity recognition. Three dimensions (3D) skeleton data is robust and somehow view-invariant. Due to this, it is one of the popular choices for human action recognition. This paper proposed using a transversal tree from 3D skeleton data to represent videos in a sequence. Further proposed two neural networks: convolutional neural network recurrent neural network_1… More >