Жарияланымдар / Публикации

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A Systematic Review of Computer Vision-Based Approaches in Educational Contexts
(Springer Science+Business Media, 2026-07-17)
Rustam Shadiev
;
Meruert Aidaraly
;
Akerke Yermukhanbet
;
Zharkyn Naraly
;
Aigul Lukhmanova
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A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities
(Science and Information Organization, 2023-01-01)
Bakһytzһan Omarov
;
N.K. Nurmash
;
Bauyrzhan Doskarayev
;
Nagashbek Zhilisbaev
;
Maxat Dairabayev
In the research paper, authors meticulously detail the development, testing, and application of an innovative deep learning model aimed at monitoring physical activities of students in real-time. Drawing upon the advanced capabilities of convolutional neural networks (CNNs), the proposed system exhibits an exceptional ability to track, analyze, and evaluate the physical exercises performed by students, thereby providing an unprecedented scope for customization in physical education strategies. This piece of scholarly work bridges the gap between physical education and cutting-edge technology, highlighting the burgeoning role of artificial intelligence in health and fitness sector. With an expansive study spanning various cohorts of physical culture students, the paper provides compelling empirical evidence that underlines the superiority of the deep learning system over conventional methods in aspects of accuracy, speed, and efficiency of monitoring. The authors demonstrate the transformative potential of their system, capable of facilitating personalized and optimized physical training strategies based on real-time feedback. Moreover, the potential implications of the study extend beyond the realm of education and into wider public health applications, with the possibility of fostering improved health outcomes on a larger scale. This research paper makes a significant contribution to the burgeoning field of AI in physical education, embodying a paradigm shift in the approach towards physical fitness and health monitoring. It underscores the potential of AI-driven technology to revolutionize traditional methods in physical education, paving the way for more personalized and effective teaching and training regimes, and ultimately contributing to enhanced health and fitness outcomes among students.
Цитирования (OpenAlex) 24Цитирования (OpenCitations) 16
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Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports
(Science and Information Organization, 2023-01-01)
Bauyrzhan Doskarayev
;
Nurlan Omarov
;
Bakһytzһan Omarov
;
Zhuldyz Ismagulova
;
Zhadra Kozhamkulova
This research paper presents the development of computer vision-enabled augmented reality games based on action detection to increase motivation for sports. With the increasing popularity of digital games, physical activity and sports participation have been declining, especially among the younger generation. To address this issue, we developed a series of augmented reality games that require players to perform physical actions to progress and succeed in the game. These games were developed using computer vision technology to detect the players' movements and provide real-time feedback, enhancing the gaming experience and promoting physical activity. The results of our user study showed that participants who played the augmented reality games reported higher levels of motivation to engage in physical activity and sports. The findings suggest that computer vision-enabled augmented reality games can be an effective tool to promote physical activity and sports participation, especially among younger generations.
Цитирования (OpenAlex) 15Цитирования (OpenCitations) 10
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Real-time augmented reality-enabled sports exercise monitoring system with personalized recommendations
(FEADEF, 2025-10-22)
Maigul Zhekambayeva
;
Perizat Akylzhan
;
Aruzhan Nazarova
;
Gulbakhar Yussupova
;
Ersayin Mailybayev
Introduction: the study examined the integration of augmented reality technologies with deep learning in sports exercise monitoring, emphasizing its importance for improving precision, accuracy of execution, and motivation in both professional and educational contexts. Objective: the objective of the research was to develop and evaluate a real-time system for monitoring and classifying sports exercises with personalized recommendations. the scope included assessing the system’s effectiveness in improving exercise accuracy, learning retention, and participant motivation during pedagogical experiments. Methodology: the system applied pose estimation, joint angle validation, and action classification using deep learning models integrated with augmented reality feedback. a controlled pedagogical experiment was carried out with two groups, one experimental group using the system and one control group with traditional instruction. data collection involved pre-tests, post-tests, delayed post-tests, and motivational questionnaires. Results: the experimental group showed significant improvements in exercise accuracy, surpassing the control group’s performance. after two weeks, retention of correct technique remained higher, and participants reported increased interest, enjoyment, and perceived usefulness of training with the proposed system. Discussion: the results were consistent with previous literature emphasizing the role of immersive and interactive technologies in improving motor learning and motivation. however, this research extended earlier findings by confirming that real-time augmented reality guidance enhances both technical performance and long-term retention of exercise skills. Conclusions: the study concluded that the proposed system represents an effective and innovative pedagogical tool for advancing sports training, and its application should be expanded to broader educational and professional environments.
Цитирования (OpenAlex) 1

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