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 - 29 April 2023

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… 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 - 29 April 2023

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… 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 - 09 February 2023

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 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 - 06 February 2023

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… More >

Sathya Bama B^*, Bevish Jinila Y

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2085-2097, 2023, DOI:10.32604/iasc.2023.028481 - 05 January 2023

Abstract Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease. This type of minimal infrastructure equipment… More >

Pavitra Vasudevan¹, R. Faerie Mattins¹, S. Srivarshan¹, Ashvath Narayanan¹, Gayatri Wadhwani¹, R. Parvathi¹, R. Maheswari^2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6039-6063, 2023, DOI:10.32604/cmc.2023.032331 - 28 December 2022

Abstract Gait refers to a person’s particular movements and stance while moving around. Although each person’s gait is unique and made up of a variety of tiny limb orientations and body positions, they all have common characteristics that help to define normalcy. Swiftly identifying such characteristics that are difficult to spot by the naked eye, can help in monitoring the elderly who require constant care and support. Analyzing silhouettes is the easiest way to assess and make any necessary adjustments for a smooth gait. It also becomes an important aspect of decision-making while analyzing and monitoring… More >

K. M. Monica, R. Parvathi^*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6229-6249, 2023, DOI:10.32604/cmc.2023.032273 - 28 December 2022

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… More >

Mohd Sadiq¹, Mohd Tauheed Khan^2,*, Sarfaraz Masood³

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5183-5200, 2022, DOI:10.32604/cmc.2022.020531 - 14 January 2022

Abstract Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the… More >

Ali Murtaza¹, Muhammad Usman Qadir¹, Muhammad Awais Khan¹, Izhar ul Haq^1,*, Kamran Shah¹, Nizar Akhtar²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1887-1899, 2022, DOI:10.32604/iasc.2022.020006 - 09 October 2021

Abstract Transfemoral prostheses have evolved from mechanical devices to microprocessor-based, electronically controlled knee joints, allowing amputees to regain control of their limbs. For improved amputee experience at varying ambulation rates, these devices provide controlled damping throughout the swing and stance phases of the gait cycle. Commercially available microprocessor-based prosthetic knee (MPK) joints use linear controllers, heuristic-based methods, and finite state machine based algorithms to track the refence gait cycle. However, since the amputee experiences a variety of non-linearities during ambulation, such as uneven terrains, walking backwards and climbing stairs, therefore, traditional controllers produces error, abnormal movements,… More >

Awais Khan¹, Muhammad Attique Khan¹, Muhammad Younus Javed¹, Majed Alhaisoni², Usman Tariq³, Seifedine Kadry⁴, Jung-In Choi⁵, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2113-2130, 2022, DOI:10.32604/cmc.2022.018270 - 27 September 2021

Abstract Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully… More >