Ch Avais Hanif¹, Muhammad Ali Mughal¹, Muhammad Attique Khan^2,3,*, Nouf Abdullah Almujally⁴, Taerang Kim⁵, Jae-Hyuk Cha⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 357-374, 2024, DOI:10.32604/cmc.2023.043061

Abstract The demand for a non-contact biometric approach for candidate identification has grown over the past ten years. Based on the most important biometric application, human gait analysis is a significant research topic in computer vision. Researchers have paid a lot of attention to gait recognition, specifically the identification of people based on their walking patterns, due to its potential to correctly identify people far away. Gait recognition systems have been used in a variety of applications, including security, medical examinations, identity management, and access control. These systems require a complex combination of technical, operational, and definitional considerations. The employment of… More >

Khursheed Aurangzeb¹, Khalid Javeed², Musaed Alhussein¹, Imad Rida³, Syed Irtaza Haider¹, Anubha Parashar^4,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 127-144, 2024, DOI:10.32604/cmc.2023.042886

Abstract Hand gestures have been used as a significant mode of communication since the advent of human civilization. By facilitating human-computer interaction (HCI), hand gesture recognition (HGRoc) technology is crucial for seamless and error-free HCI. HGRoc technology is pivotal in healthcare and communication for the deaf community. Despite significant advancements in computer vision-based gesture recognition for language understanding, two considerable challenges persist in this field: (a) limited and common gestures are considered, (b) processing multiple channels of information across a network takes huge computational time during discriminative feature extraction. Therefore, a novel hand vision-based convolutional neural network (CNN) model named (HVCNNM)… More >

Ramiz Gorkem Birdal^*, Ahmet Sertbas

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2727-2744, 2023, DOI:10.32604/cmc.2023.032069

Abstract Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’ walking patterns to be recognized. Existing research in this area has primarily focused on feature analysis through the extraction of individual features, which captures most of the information but fails to capture subtle variations in gait dynamics. Therefore, a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced. The gait features extracted from body halves divided by anatomical planes on vertical, horizontal, and diagonal axes are grouped to form… More >

Shoutao Li^1,2,*, Ruyi Qu¹, Yu Zhang¹, Dingli Yu³

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2809-2823, 2023, DOI:10.32604/iasc.2023.036999

Abstract Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and… More >

Sang-Soo Yeo¹, Seungmin Rho^2,*, Hyungjoon Kim³, Jibran Safdar⁴, Umar Zia⁵, Mehr Yahya Durrani⁵

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2027-2047, 2023, DOI:10.32604/csse.2023.040327

Abstract Intelligent vision-based surveillance systems are designed to deal with the gigantic volume of videos captured in a particular environment to perform the interpretation of scenes in form of detection, tracking, monitoring, behavioral analysis, and retrievals. In addition to that, another evolving way of surveillance systems in a particular environment is human gait-based surveillance. In the existing research, several methodological frameworks are designed to use deep learning and traditional methods, nevertheless, the accuracies of these methods drop substantially when they are subjected to covariate conditions. These covariate variables disrupt the gait features and hence the recognition of subjects becomes difficult. To… More >

C. S. S. Anupama¹, Rafina Zakieva², Afanasiy Sergin³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Chomyong Kim⁸, Yunyoung Nam^8,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321

Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning… More >

Ch Avais Hanif¹, Muhammad Ali Mughal^1,*, Muhammad Attique Khan², Usman Tariq³, Ye Jin Kim⁴, Jae-Hyuk Cha⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5123-5140, 2023, DOI:10.32604/cmc.2023.038120

Abstract Gait recognition is an active research area that uses a walking theme to identify the subject correctly. Human Gait Recognition (HGR) is performed without any cooperation from the individual. However, in practice, it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a coat. Researchers, over the years, have worked on successfully identifying subjects using different techniques, but there is still room for improvement in accuracy due to these covariant factors. This paper proposes an automated model-free framework for human gait recognition in this article. There are a… More >

Muhammad Attique Khan¹, Awais Khan¹, Majed Alhaisoni², Abdullah Alqahtani³, Ammar Armghan⁴, Sara A. Althubiti⁵, Fayadh Alenezi⁴, Senghour Mey⁶, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5087-5103, 2023, DOI:10.32604/cmc.2023.033856

Abstract Human gait recognition (HGR) is the process of identifying a subject (human) based on their walking pattern. Each subject is a unique walking pattern and cannot be simulated by other subjects. But, gait recognition is not easy and makes the system difficult if any object is carried by a subject, such as a bag or coat. This article proposes an automated architecture based on deep features optimization for HGR. To our knowledge, it is the first architecture in which features are fused using multiset canonical correlation analysis (MCCA). In the proposed method, original video frames are processed for all 11… More >

Kainat Ibrar¹, Abdul Muiz Fayyaz¹, Muhammad Attique Khan², Majed Alhaisoni³, Usman Tariq⁴, Seob Jeon⁵, Yunyoung Nam^6,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2351-2368, 2023, DOI:10.32604/csse.2023.036185

Abstract Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains. Gait is a person’s identity that can reflect reliable information about his mood, emotions, and substantial personality traits under scrutiny. This research focuses on recognizing key personality traits, including neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness, in line with the big-five model of personality. We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors. For experimentation, we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks… More >

Muhammad Azhar^1,*, Sehat Ullah¹, Khalil Ullah², Habib Shah³, Abdallah Namoun⁴, Khaliq Ur Rahman⁵

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 165-182, 2023, DOI:10.32604/cmc.2023.034605

Abstract Human biometric analysis has gotten much attention due to its widespread use in different research areas, such as security, surveillance, health, human identification, and classification. Human gait is one of the key human traits that can identify and classify humans based on their age, gender, and ethnicity. Different approaches have been proposed for the estimation of human age based on gait so far. However, challenges are there, for which an efficient, low-cost technique or algorithm is needed. In this paper, we propose a three-dimensional real-time gait-based age detection system using a machine learning approach. The proposed system consists of training… More >