Sahbi Boubaker^1,2,*, Salim Hadj Said³, Souad Kamel¹, Habib Dimassi³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5703-5721, 2025, DOI:10.32604/cmc.2025.069047 - 23 October 2025

Abstract Accurate trajectory tracking in lower-limb exoskeletons is challenged by the nonlinear, time-varying dynamics of human-robot interaction, limited sensor availability, and unknown external disturbances. This study proposes a novel control strategy that combines flatness-based control with two cascaded observers: a high-gain observer to estimate unmeasured joint velocities, and a nonlinear disturbance observer to reconstruct external torque disturbances in real time. These estimates are integrated into the control law to enable robust, state-feedback-based trajectory tracking. The approach is validated through simulation scenarios involving partial state measurements and abrupt external torque perturbations, reflecting realistic rehabilitation conditions. Results confirm More >

Mohammad Soleimani Amiri¹, Sahbi Boubaker^2,3,*, Rizauddin Ramli^4,*, Souad Kamel²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3113-3133, 2025, DOI:10.32604/cmes.2025.069167 - 30 September 2025

Abstract Disability is defined as a condition that makes it difficult for a person to perform certain vital activities. In recent years, the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent. However, controlling an exoskeleton for rehabilitation presents challenges due to their non-linear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton. To remedy these problems, this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons, addressing the challenges of nonlinear dynamics and external disturbances. The proposed controller integrated… More >

Subrata Kumer Paul^1,2, Abu Saleh Musa Miah^3,4, Rakhi Rani Paul^1,2, Md. Ekramul Hamid², Jungpil Shin^4,*, Md Abdur Rahim⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2513-2530, 2025, DOI:10.32604/cmc.2025.063563 - 03 July 2025

Abstract The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep… More >

Iman Ezzat, Marisa Zallocchi^*

BIOCELL, Vol.49, No.5, pp. 789-811, 2025, DOI:10.32604/biocell.2025.062325 - 27 May 2025

Abstract Integrins are heterodimeric transmembrane receptors that mediate bidirectional interactions between the intracellular cytoskeletal array and the extracellular matrix. These interactions are critical in tissue development and function by regulating gene expression and sustaining tissue architecture. In humans, the integrin family is composed of 18 alpha (α) and 8 beta (β) subunits, constituting 24 distinct αβ combinations. Based on their structure and ligand-binding properties, only a subset of integrins, 8 out of 24, recognizes the arginine-glycine-aspartate (RGD) tripeptide motif in the native ligand. One of the major RGD binding integrins is integrin alpha 8 beta 1 More >

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 >