Hammad Rustam¹, Muhammad Muneeb¹, Suliman A. Alsuhibany², Yazeed Yasin Ghadi³, Tamara Al Shloul⁴, Ahmad Jalal¹, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2331-2346, 2023, DOI:10.32604/cmc.2023.028712

Abstract Hand gesture recognition (HGR) is used in a numerous applications, including medical health-care, industrial purpose and sports detection. We have developed a real-time hand gesture recognition system using inertial sensors for the smart home application. Developing such a model facilitates the medical health field (elders or disabled ones). Home automation has also been proven to be a tremendous benefit for the elderly and disabled. Residents are admitted to smart homes for comfort, luxury, improved quality of life, and protection against intrusion and burglars. This paper proposes a novel system that uses principal component analysis, linear discrimination analysis feature extraction, and… More >

Sakorn Mekruksavanich¹, Anuchit Jitpattanakul^2,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2111-2128, 2023, DOI:10.32604/iasc.2023.033542

Abstract The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturing firm are vital for the rapid and accurate diagnosis of work performance, particularly during the training of a new worker. Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques. Despite widespread computer vision-based approaches, it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where camera deployment is problematic. Through the use of wearable inertial sensors, we propose a deep learning method for automatically recognizing the activities of construction workers. The… More >

Umair Muneer Butt^1,2,*, Hadiqa Aman Ullah², Sukumar Letchmunan¹, Iqra Tariq², Fadratul Hafinaz Hassan¹, Tieng Wei Koh³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5017-5033, 2023, DOI:10.32604/cmc.2023.035512

Abstract Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models… More >

K. Ishwarya, A. Alice Nithya^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6081-6099, 2023, DOI:10.32604/cmc.2023.034654

Abstract Human pose estimation (HPE) is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision (CV) communities. HPE finds its applications in several fields namely activity recognition and human-computer interface. Despite the benefits of HPE, it is still a challenging process due to the variations in visual appearances, lighting, occlusions, dimensionality, etc. To resolve these issues, this paper presents a squirrel search optimization with a deep convolutional neural network for HPE (SSDCNN-HPE) technique. The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.… More >

K. M. Monica, R. Parvathi^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6229-6249, 2023, DOI:10.32604/cmc.2023.032273

Abstract Human Activity Recognition (HAR) has always been a difficult task to tackle. It is mainly used in security surveillance, human-computer interaction, and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things (IoT). Human Activity Recognition data can be recorded with the help of sensors, images, or smartphones. Recognizing daily routine-based human activities such as walking, standing, sitting, etc., could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network (2D CNN) MODEL, Long Short Term Memory (LSTM) Model, Bidirectional long short-term memory (Bi-LSTM) are used… More >

Mohammed Alonazi¹, Mrim M. Alnfiai^2,*

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3135-3150, 2023, DOI:10.32604/csse.2023.034124

Abstract Human-Computer Interaction (HCI) is a sub-area within computer science focused on the study of the communication between people (users) and computers and the evaluation, implementation, and design of user interfaces for computer systems. HCI has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive science. Usability is an aspect of HCI dedicated to guaranteeing that human–computer communication is, amongst other things, efficient, effective, and sustaining for the user. Simultaneously, Human activity recognition (HAR) aim is to identify actions from a sequence of observations on the activities of subjects and… More >

S. Nandagopal^1,*, G. Karthy², A. Sheryl Oliver³, M. Subha⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1719-1733, 2023, DOI:10.32604/csse.2023.028003

Abstract Human Action Recognition (HAR) and pose estimation from videos have gained significant attention among research communities due to its application in several areas namely intelligent surveillance, human robot interaction, robot vision, etc. Though considerable improvements have been made in recent days, design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle, occlusion, background, movement speed, and so on. From the literature, it is observed that hard to deal with the temporal dimension in the action recognition process. Convolutional neural network (CNN) models could… More >

E. Dhiravidachelvi¹, M.Suresh Kumar², L. D. Vijay Anand³, D. Pritima⁴, Seifedine Kadry⁵, Byeong-Gwon Kang⁶, Yunyoung Nam^7,*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 961-977, 2023, DOI:10.32604/csse.2023.024612

Abstract Human Activity Recognition (HAR) has been made simple in recent years, thanks to recent advancements made in Artificial Intelligence (AI) techniques. These techniques are applied in several areas like security, surveillance, healthcare, human-robot interaction, and entertainment. Since wearable sensor-based HAR system includes in-built sensors, human activities can be categorized based on sensor values. Further, it can also be employed in other applications such as gait diagnosis, observation of children/adult’s cognitive nature, stroke-patient hospital direction, Epilepsy and Parkinson’s disease examination, etc. Recently-developed Artificial Intelligence (AI) techniques, especially Deep Learning (DL) models can be deployed to accomplish effective outcomes on HAR process.… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1245-1259, 2023, DOI:10.32604/iasc.2023.028290

Abstract Smoking is a major cause of cancer, heart disease and other afflictions that lead to early mortality. An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives. Smoking activities often accompany other activities such as drinking or eating. Consequently, smoking activity recognition can be a challenging topic in human activity recognition (HAR). A deep learning framework for smoking activity recognition (SAR) employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules (ResNetSE) to increase the effectiveness of the SAR framework. The proposed model was tested against… More >

Sakorn Mekruksavanich¹, Anuchit Jitpattanakul^2,*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1907-1925, 2022, DOI:10.32604/iasc.2022.027233

Abstract Numerous learning-based techniques for effective human activity recognition (HAR) have recently been developed. Wearable inertial sensors are critical for HAR studies to characterize sport-related activities. Smart wearables are now ubiquitous and can benefit people of all ages. HAR investigations typically involve sensor-based evaluation. Sport-related activities are unpredictable and have historically been classified as complex, with conventional machine learning (ML) algorithms applied to resolve HAR issues. The efficiency of machine learning techniques in categorizing data is limited by the human-crafted feature extraction procedure. A deep learning model named MBiGRU (multimodal bidirectional gated recurrent unit) neural network was proposed to recognize everyday… More >