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Javid Iqbal

Bio: Javid Iqbal is an academic researcher from University of Kuala Lumpur. The author has contributed to research in topics: Dreyfus model of skill acquisition & Dance. The author has co-authored 1 publications.

Papers
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Journal ArticleDOI
TL;DR: The obtained results support a general acceptance towards ARDTS among the users who are interested in exploring the cutting-edge technology of AR for gaining expertise in a dance skill.
Abstract: The advancement in Computer Vision (CV) has evolved drastically from image processing to object recognition, tracking video, restoration of images, three-dimensional (3D) pose recognition, and emotion analysis These advancements have eventually led to the birth of Augmented Reality (AR) technology, which means embedding virtual objects into the real-world environment The primary focus of this research was to solve the long-term learning retention and poor learning efficiency for mastering a dance skill through the AR technology based on constructivism learning theory, Dreyfus model and Technology Acceptance Model (TAM) The problem analysis carried out in this research had major research findings, in which the retention and learning efficiency of a dance training system were predominantly determined through the type of learning theory adopted, learning environment, training tools, skill acquisition technology and type of AR technique Therefore, the influential factors for the user acceptance of AR-based dance training system (ARDTS) were based on quantitative analysis These influential factors were determined to address the problem of knowledge gap on acceptance of AR-based systems for dance education through self-learning The evaluation and testing were conducted to validate the developed and implemented ARDTS system The Technology Acceptance Model (TAM) as the evaluation model and quantitative analysis was done with a research instrument that encompassed external and internal variables TAM consisted of 37 items, in which six factors were used to assess the new developed ARDTS by the authors and its acceptability among 86 subjects The current study had investigated the potential use of AR-based dance training system to promote a particular dance skill among a sample population with various backgrounds and interests The obtained results support a general acceptance towards ARDTS among the users who are interested in exploring the cutting-edge technology of AR for gaining expertise in a dance skill

13 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper , the authors evaluated the adoption or acceptability of the metaverse system in education from the viewpoint of information systems (IS) theories/models and provided a thorough pointer that might help the scholars to carry out additional research in metaverse acceptance.
Abstract: The evaluation of information systems (IS) models, which are employed to research the adoption or acceptance of metaverse systems, is thought to be a subject of major significance. Studying the adoption or acceptability of the metaverse system is not a recent study area, and many academics have taken on the task. We should be acquainted with the leading IS models used in this study trend to assess these models and give academics a comprehensive understanding of this study trend. The primary goal of this research, in contrast to previous reviews, is to systematically evaluate the metaverse research in education from the viewpoint of IS theories/models to offer a thorough pointer that might help the scholars to carry out additional research in metaverse acceptance. A total of 41 research that was published between 2011 and 2022 were examined in the present systematic review. The main study results showed that the Technology Acceptance Model (TAM) is recognized as the most widely used model in forecasting people’s intentions to uphold the metaverse system. Furthermore, it was discovered that SmartPLS (PLS-SEM) is a typical tool for validating metaverse models. In addition, the key research purpose covered in the bulk of the reviewed research is to study how students adopt or accept the metaverse system and the technology that supports it. Additionally, most of the research that was gathered was done in China, Taiwan, and the USA, accordingly. Additionally, in most of the evaluated research, it was discovered that university students were the primary respondents concerning data acquisition. These findings are anticipated to significantly improve both our comprehension of metaverse system study and the utilization of IS models.

13 citations

Journal ArticleDOI
TL;DR: A comprehensive view of approaches proposed in the various fields of computerized dance modeling that aid in cultural heritage preservation is presented in this paper , where the authors have developed various approaches to automate the dance, identify the gesture, poses and stance (Pose Recognition), recognize the dance forms, dance movement classification, etc.

2 citations

Proceedings ArticleDOI
09 Jan 2022
TL;DR: In this paper , a mixed-reality human-machine interface is presented to facilitate the practice of ballroom dance without the need of a human instructor nor human contact. But, due to the measures that are taken globally against the COVID19, a great number of training facilities have partially or completely restricted access to their trainers.
Abstract: Mastering ballroom dance requires learning motor skills such as synchronizing the body motion to a tempo as well as memorizing long sequences of dance steps. Traditionally, the acquisition of these skills is guided by an instructor, who adapts the training to the level of the trainee. However, due to the measures that are taken globally against the COVID19, a great number of training facilities have partially or completely restricted access to their trainers. To facilitate the practice of ballroom dance without the need of a human instructor nor human contact, this paper presents a mixed-reality human-machine interface that provides part practice of ballroom dance. It achieves it by fractionalizing the dance into only the footwork and conveying the tempo, position trajectory, and velocity trajectory with multimodal feedback. The multimodal feedback is generated with a floor projection, vibrotactile actuators, and a metronome, which provide visual, haptic, and auditory feedback. In an experiment with 15 participants, results suggest that the motor learning of dance skills occurs when visual and haptic feedback is present, whereas it is further improved when the three modalities (visual, haptic, and auditory) are employed during training.

1 citations

Journal ArticleDOI
TL;DR: The tracking algorithm proposed in this paper has higher robustness than other algorithms and effectively reduces the error samples generated during the tracking process, thus improving the accuracy of long-term tracking.
Abstract: Due to the complex posture changes in dance movements, accurate detection and tracking of human targets are carried out in order to improve the guidance ability of dancers in ethnic areas. A multifeature fusion-based tracking algorithm for dancers in ethnic areas is proposed. The edge contour model of video images of dancers in ethnic areas is detected, and the video tracking scanning imaging model of dancers in ethnic areas is constructed. The video images of dancers in ethnic areas are enhanced based on the initial contour distribution, and a visual perception model of dancers tracking images in ethnic areas is established. To improve the algorithm’s estimation of complex poses and finally complete the dance movement recognition, a feature pyramid network is used to extract the features of dance movements, and then, a multifeature fusion module is used to fuse multiple features. The tracking algorithm proposed in this paper has higher robustness than other algorithms and effectively reduces the error samples generated during the tracking process, thus improving the accuracy of long-term tracking.

1 citations

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors studied human motion recognition in dance video images based on an attitude estimation algorithm, which can be used not only for professional dancers' movement correction, dance self-help teaching, and other application scenarios but also for athletes' movement analysis.
Abstract: With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers’ movement correction, dance self-help teaching, and other application scenarios but also for athletes’ movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers’ movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms.