Exame do sistema de monitoramento de exercícios de realidade aumentada como ferramenta auxiliar para futuros formadores de professores

Autores

  • Bakhytzhan Omarov International University of Tourism and Hospitality
  • Nurlan Omarov Al-Farabi Kazakh National University
  • Quwanishbay Mamutov Nukus branch of the Institute for Retraining and Professional Development of Specialists in Physical Education and Sport
  • Zhanibek Kissebayev Department of Anatomy, Physiology and Sports Medicine, Kazakh Academy of Sports and Tourism Almaty, Kazakhstan
  • Almas Anarbayev International University of Tourism and Hospitality
  • Adilbay Tastanov Kazakh Academy of Sport and Tourism
  • Zhandos Yessirkepov International University of Tourism and Hospitality

DOI:

https://doi.org/10.47197/retos.v58.105030

Palavras-chave:

exercise monitoring system, real-time feedback, athlete performance, innovative instructional approaches, motivation, muscle injury, educational technology, fitness training

Resumo

This study explores the effectiveness of exercise monitoring systems in improving athlete performance and motivation within educational settings. Two hypotheses were formulated and tested: one positing that the utilization of exercise monitoring systems would reduce muscle injury rates among athletes, and the other suggesting that it would increase athletes' motivation levels. The experimental design involved dividing participants into experimental and control groups, with the former utilizing the proposed exercise monitoring system and the latter employing traditional teaching methods. Assessments were conducted post-session to measure comprehension and motivation levels, with evaluation criteria focusing on the accurate identification of course components. Contrary to expectations, the results did not support the hypotheses, indicating no significant reduction in muscle injury rates or increase in motivation levels among athletes exposed to the monitoring system. These findings underscore the need for a nuanced understanding of the complex factors influencing athlete development and performance outcomes. Future research should employ rigorous methodologies and objective outcome measures to further elucidate the role of exercise monitoring systems in athlete development and optimize their integration into training programs, thus contributing to advancements in athlete performance and motivation in educational contexts.

Keywords: exercise monitoring system, real-time feedback, athlete performance, innovative instructional approaches, motivation, muscle injury, educational technology, fitness training.

Referências

Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 140527.

Aju, A., Mathew, C., & Prakasi, O. S. (2022). PoseNet based Model for Estimation of Karate Poses. Journal of Innovative Image Processing, 4(1), 16-25.

Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Egwuonwu, C. A., Onu, F. U., Alo, U. R., & Teh, Y. W. (2022). Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Systems with Applications, 202, 117362.

Batra, P., & Dave, D. M. (2024). Revolutionizing Healthcare Platforms: The Impact of AI on Patient Engagement and Treatment Efficacy. International Journal of Science and Research (IJSR), 13(10.21275), 613-624.

Cai, Y., Wang, Z., Luo, Z., Yin, B., Du, A., Wang, H., ... & Sun, J. (2020). Learning delicate local representations for multi-person pose estimation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16 (pp. 455-472). Springer International Publishing.

Cao, F., Xiang, M., Chen, K., & Lei, M. (2022). Intelligent physical education teaching tracking system based on multimedia data analysis and artificial intelligence. Mobile Information Systems, 2022.

Chen, S., Wang, Z., & Prisacariu, V. (2021, December). Direct-posenet: Absolute pose regression with photometric consistency. In 2021 International Conference on 3D Vision (3DV) (pp. 1175-1185). IEEE.

Chua, J., Ong, L. Y., & Leow, M. C. (2021). Telehealth using PoseNet-based system for in-home rehabilitation. Future Internet, 13(7), 173.

Claes, J., Cornelissen, V., McDermott, C., Moyna, N., Pattyn, N., Cornelis, N., ... & Buys, R. (2020). Feasibility, acceptability, and clinical effectiveness of a technology-enabled cardiac rehabilitation platform (Physical Activity Toward Health-I): randomized controlled trial. Journal of Medical Internet Research, 22(2), e14221.

Cossich, V. R., Carlgren, D., Holash, R. J., & Katz, L. (2023). Technological Breakthroughs in Sport: Current Practice and Future Potential of Artificial Intelligence, Virtual Reality, Augmented Reality, and Modern Data Visualization in Performance Analysis. Applied Sciences, 13(23), 12965.

Daling, L. M., & Schlittmeier, S. J. (2024). Effects of augmented reality-, virtual reality-, and mixed reality–based training on objective performance measures and subjective evaluations in manual assembly tasks: a scoping review. Human factors, 66(2), 589-626.

Ehioghae, M., Montoya, A., Keshav, R., Vippa, T. K., Manuk-Hakobyan, H., Hasoon, J., ... & Urits, I. (2024). Effectiveness of Virtual Reality–Based Rehabilitation Interventions in Improving Postoperative Outcomes for Orthopedic Surgery Patients. Current Pain and Headache Reports, 28(1), 37-45.

Fang, Y. (2024). Utilizing Wearable Technology to Enhance Training and Performance Monitoring in Indonesian Badminton Players. Studies in Sports Science and Physical Education, 2(1), 11-23.

Flores Ferro, E., Maureira Cid, F., Hadweh Briceño, M., Gavotto Nogales, O., Gutiérrez Duarte, S. A., Vergara Jiménez, J., & Mandujano Jara, S. (2022). A un año de las clases virtuales en la carrera de Educación Física: una comparación Chile y México (A year away from virtual classes in the physical education career: a comparison Chile and Mexico). Retos, 45, 138–143. https://doi.org/10.47197/retos.v45i0.91944

García-Bravo, S., Cuesta-Gómez, A., Campuzano-Ruiz, R., López-Navas, M. J., Domínguez-Paniagua, J., Araújo-Narváez, A., ... & Cano-de-la-Cuerda, R. (2021). Virtual reality and video games in cardiac rehabilitation programs. A systematic review. Disability and Rehabilitation, 43(4), 448-457.

Hutajulu, O. Y., Mendoza, M. D., Astrid, E., & Rahmadani, R. (2024, January). Utilizing Internet of Things Technology in the Development of AC Electrical Circuit Trainer Module. In Proceedings of the 5th International Conference on Innovation in Education, Science, and Culture, ICIESC 2023, 24 October 2023, Medan, Indonesia.

Kaulage, A., Mane, D., Upadhye, G., Rajput, S. D., Kale, S., & Zope, B. (2024). Exercise Movement Detection Using Spearman Correlation-based Sliding Window Technique. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 48-54.

Klochko, O. V., & Fedorets, V. M. (2022). Using immersive reality technologies to increase a physical education teacher's health-preserving competency. Educational Technology Quarterly, 2022(4), 276-306.

Li, G. Y., Li, J., Li, Z. J., Zhang, Y. P., Zhang, X., Wang, Z. J., ... & Zhang, H. D. (2022). Hierarchical PVDF-HFP/ZnO composite nanofiber–based highly sensitive piezoelectric sensor for wireless workout monitoring. Advanced Composites and Hybrid Materials, 5(2), 766-775.

Lin, K. C., Ko, C. W., Hung, H. C., & Chen, N. S. (2023). The effect of real-time pose recognition on badminton learning performance. Interactive Learning Environments, 31(8), 4772-4786.

Omarov, B., Nurmash, N., Doskarayev, B., Zhilisbaev, N., Dairabayev, M., Orazov, S., & Omarov, N. (2023). A Novel Deep Neural Network to Analyze and Monitoring the Physical Training Relation to Sports Activities. International Journal of Advanced Computer Science and Applications, 14(9).

Pacheco-Godoy, B. ., Godoy, F. ., Paz Martínez, F. ., Lamas, C., Arellano-Correa, S. ., Villarroel-Ojeda, L. ., Gutiérrez-Turner, E. ., & La Placa-Ubeda, J. . (2024). Characterization of physical exercise with virtual guidance and perception of its benefits in Chilean women. Retos, 52, 204–210. https://doi.org/10.47197/retos.v52.99670

Potvin, C. (2020). ANOVA: experiments in controlled environments. In Design and analysis of ecological experiments (pp. 46-68). Chapman and Hall/CRC.

Rao, Y., Zhao, W., Tang, Y., Zhou, J., Lim, S. N., & Lu, J. (2022). Hornet: Efficient high-order spatial interactions with recursive gated convolutions. Advances in Neural Information Processing Systems, 35, 10353-10366.

Ricci, S., Calandrino, A., Borgonovo, G., Chirico, M., & Casadio, M. (2022). Virtual and augmented reality in basic and advanced life support training. JMIR Serious Games, 10(1), e28595.

Rodriguez-Fuentes, G. ., Campo-Prieto, P., Souto, X. C., & Cancela Carral, J. M. (2024). Realidad virtual inmersiva y su influencia en parámetros fisiológicos de personas sanas (Immersive virtual reality and its influence on physiological parameters in healthy people). Retos, 51, 615–625. https://doi.org/10.47197/retos.v51.101164

Romero Parra, R. M., Barboza Arenas, L. A., Espina-Romero, L. C., Rodríguez Ángeles, C. H., Romero Chacin, J. L., Garcés Rosendo., E. J., Faría Romero, J. A., & Vertiz Osores, R. I. (2023). Efectos de un gym neuróbico en el rendimiento académico de los estudiantes universitarios en entornos virtuales (Effects of a neurobic gym on the academic performance of university students in virtual environments). Retos, 50, 371–379. https://doi.org/10.47197/retos.v50.93788

Seah, M. L. C., & Koh, K. T. (2021). The efficacy of using mobile applications in changing adolescent girls’ physical activity behaviour during weekends. European Physical Education Review, 27(1), 113-131.

Shi, B., Xu, Y., Dai, W., Wang, B., Zhang, S., Li, C., ... & Xiong, H. (2020, October). Tiny-Hourglassnet: An efficient design for 3D human pose estimation. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 1491-1495). IEEE.

Solas-Martínez, J. L., Suárez-Manzano, S., De la Torre-Cruz, M. J., & Ruiz-Ariza, A. (2023). Artificial Intelligence and Augmented Reality in Physical Activity: A Review of Systems and Devices. Augmented Reality and Artificial Intelligence: The Fusion of Advanced Technologies, 245-270.

Soltani, P., & Morice, A. H. (2020). Augmented reality tools for sports education and training. Computers & Education, 155, 103923.

Tariq, M. U. (2024). Revolutionizing Health Data Management with Blockchain Technology: Enhancing Security and Efficiency in a Digital Era. In Emerging Technologies for Health Literacy and Medical Practice (pp. 153-175). IGI Global.

Vashishth, T. K., Sharma, V., Sharma, K. K., Kumar, B., Panwar, R., & Chaudhary, S. (2024). AI-Driven Learning Analytics for Personalized Feedback and Assessment in Higher Education. In Using Traditional Design Methods to Enhance AI-Driven Decision Making (pp. 206-230). IGI Global.

Wang, T., & Wu, D. (2024). Computer-Aided Traditional Art Design Based on Artificial Intelligence and Human-Computer Interaction. Computer-Aided Design and Applications, 21, 59-73.

Ye, M., Shen, J., Zhang, X., Yuen, P. C., & Chang, S. F. (2020). Augmentation invariant and instance spreading feature for softmax embedding. IEEE transactions on pattern analysis and machine intelligence, 44(2), 924-939.

Yi, Z., Chen, Y. H., Yin, Y., Cheng, K., Wang, Y., Nguyen, D., ... & Kim, E. (2022). Brief research report: A comparison of robust tests for homogeneity of variance in factorial ANOVA. The Journal of Experimental Education, 90(2), 505-520.

Downloads

Publicado

2024-06-22

Como Citar

Omarov, B., Omarov, N., Mamutov, Q., Kissebayev, Z., Anarbayev, A., Tastanov, A., & Yessirkepov, Z. (2024). Exame do sistema de monitoramento de exercícios de realidade aumentada como ferramenta auxiliar para futuros formadores de professores. Retos, 58, 85–94. https://doi.org/10.47197/retos.v58.105030

Edição

Secção

Artigos de caráter científico: trabalhos de pesquisas básicas e/ou aplicadas.