Artificial Intelligence and Anatomical-Physiological Parameters: Evidence-Based Embodied Approach
Parole chiave:
Pedagogical Approach in Sports; Athletic Performance Optimization; EducationAbstract
Riferimenti bibliografici
Suo, X., Tang, W., Mao, L., & Li, Z. (2024). Digital human and embodied intelligence for sports science: advancements, opportunities and prospects. The Visual Computer, 1-17.
Arabameri, E. (2024). The Evolution of Motor Behavior: Lessons from Past Research and Future Pro-spects. Health, 2(4), 134-151.
Harris, E. J., Khoo, I. H., & Demircan, E. (2022). A survey of human gait-based artificial intelligence ap-plications. Frontiers in Robotics and AI, 8, 749274.
Ramkumar, P. N., Luu, B. C., Haeberle, H. S., Karnuta, J. M., Nwachukwu, B. U., & Williams, R. J. (2022). Sports medicine and artificial intelligence: a primer. The American Journal of Sports Medicine, 50(4), 1166-1174.
Farina, M. (2021). Embodied cognition: dimensions, domains and applications. Adaptive Behavior, 29(1), 73-88.
Wexler, M. N., & Oberlander, J. (2023). Homo Athletica to Homo Digitalis: Esports as Sport. The Midwest Quarterly, 65(1), 90-9.
Patsantaras, N. (2020). Virtual bodies (avatars) and sport exercises: some important thoughts. European Journal for Sport and Society, 17(4), 339-356.
Alimuddin, A., Nazri, S. B. M., Liza, L., Pebriyani, D., & Muchlis, A. P. (2024). Physical education and sport essential as transversality and body integration in the learning process: A systematic review. Retos: nuevas tendencias en educación física, deporte y recreación, (58), 20-27.
North, C., Hills, D., Maher, P., Farkić, J., Zeilmann, V., Waite, S., ... & French, G. (2024). The impact of ar-tificial intelligence on adventure education and outdoor learning: international perspectives. Journal of Adventure Education and Outdoor Learning, 24(1), 123-140.
Ma, C. (2024). The Influence of College Physical Education Teaching on Students' Mental Health and Skill Improvement under the embodied cognition Theory. Revista de Psicología del Deporte (Journal of Sport Psycho-logy), 33(2), 366-375.
Ali, L., Sorrentino, C., Vivona, A., & Martiniello, L. (2023). Bridging the Gap between the Body and the Machine: Embodied Learning with Interventional Brain Computer Interfaces?. Sport Mont, 21(3).
Paloma, F. G., & Tafuri, D. (2016). Embodied Cognition. Body, movement and sport for didactics. Italian Journal of Educational Research, (17), 41-52.
Kewalramani, S., Kidman, G., & Palaiologou, I. (2021). Using artificial intelligence (AI)-interfaced robotic toys in early childhood settings: A case for children’s inquiry literacy. European Early Childhood Education Research Journal, 29(5), 652-668.
Southgate, D. F., Prinold, J. A., & Weinert-Aplin, R. A. (2016). Motion analysis in sport. In Sports innova-tion, technology and research (pp. 3-30).
RUCCO, R., ASCIONE, A., & DI PALMA, D. A. V. I. D. E. (2020). Motion analysis in sport training: The link between technology and pedagogy. Journal of Physical Education and Sport, 20(Supplement Issue 4), 2337-2341.
Ortega, B. P., & Olmedo, J. M. J. (2017). Application of motion capture technology for sport performance analysis. Retos: nuevas tendencias en educación física, deporte y recreación, (32), 241-247.
Roggio, F., Ravalli, S., Maugeri, G., Bianco, A., Palma, A., Di Rosa, M., & Musumeci, G. (2021). Techno-logical advancements in the analysis of human motion and posture management through digital devi-ces. World journal of orthopedics, 12(7), 467.
Jauhiainen, S., Kauppi, J. P., Leppänen, M., Pasanen, K., Parkkari, J., Vasankari, T., ... & Äyrämö, S. (2021). New machine learning approach for detection of injury risk factors in young team sport athle-tes. International journal of sports medicine, 42(02), 175-182.
Wang, P. (2021). Research on sports training action recognition based on deep learning. Scientific Program-ming, 2021(1), 3396878.
Fang, L., & Sun, M. (2021). Motion recognition technology of badminton players in sports video ima-ges. Future Generation Computer Systems, 124, 381-389.
Coppola, S., Costa, C., & Vastola, R. (2024). Comparative study of stag leap performance in rhythmic gymnastics: Motion analysis of two different take-off techniques. JOURNAL OF PHYSICAL EDU-CATION AND SPORT, 1299-1306.
Blandeau, M., Guichard, R., Hubaut, R., & Leteneur, S. (2023). IMU positioning affects range of motion measurement during squat motion analysis. Journal of Biomechanics, 153, 111598.
Lee, K. (2023). Motion Analysis of Core Stabilization Exercise in Women: Kinematics and Electromyo-graphic Analysis. Sports, 11(3), 66.
MacDonald, J. P., Pape, M., Ackerman, K. E., Carneiro, E., Huang, Y., Rizzone, K. H., ... & Mountjoy, M. (2024). The digital mirror: how generative artificial intelligence reflects and amplifies gender bias. British journal of sports medicine, bjsports-2024.
Putze, T., Raguse, K., & Maas, H. G. (2007, January). Configuration of multi mirror systems for single high-speed camera based 3D motion analysis. In Videometrics IX (Vol. 6491, pp. 185-194). SPIE.
Gong, C., & Mehrl, D. (2014). Characterization of the digital micromirror devices. IEEE Transactions on Electron Devices, 61(12), 4210-4215.
Frey, M., Giovanoli, P., Gerber, H., Slameczka, M., & Stüssi, E. (1999). Three-dimensional video analysis of facial movements: a new method to assess the quantity and quality of the smile. Plastic and reconstructive surgery, 104(7), 2032-2039.
Preonas, D. D., & Prater, R. F. (1970). Quantitative Motion Analysis from Rotating Mirror Framing Camera Records. Journal of the SMPTE, 79(7), 586-589.
Chen, B., & Pan, B. (2022). Mirror-assisted multi-view digital image correlation: Principles, applications and implementations. Optics and Lasers in Engineering, 149, 106786.
Kreis, T., Aswendt, P., & Ho¨ fling, R. (2001). Hologram reconstruction using a digital micromirror de-vice. Optical Engineering, 40(6), 926-933.
Trout, J. (2013). Digital movement analysis in physical education. Journal of Physical Education, Recreation & Dance, 84(7), 47-50.
Solntsev, I. V. (2021). Application of Innovative Digital Products in Sports Industry. Strategic decisions and risk management, 12(2), 184-189.
Anderson, F., Grossman, T., Matejka, J., & Fitzmaurice, G. (2013). YouMove: enhancing movement training with an augmented reality mirror. In Proceedings of the 26th annual ACM symposium on User interface software and technology (pp. 311-320).
Chen, J. (2022). [Retracted] 3D Visualization Analysis of Motion Trajectory of Knee Joint in Sports Training Based on Digital Twin. Computational Intelligence and Neuroscience, 2022(1), 3988166.
Hülsmann, F., Frank, C., Senna, I., Ernst, M. O., Schack, T., & Botsch, M. (2019). Superimposed skilled performance in a virtual mirror improves motor performance and cognitive representation of a full body motor action. Frontiers in Robotics and AI, 6, 43.
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.
Venek, V., Kranzinger, S., Schwameder, H., & Stoeggl, T. (2022). Human movement quality assessment using sensor technologies in recreational and professional sports: a scoping review. Sensors, 22(13), 4786.
Habibi, M., Nourani, M., & Nourani, M. (2024, June). AI-Based Kinematic Analysis for Track Athletes. In 2024 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 338-343). IEEE.
##submission.downloads##
Pubblicato
Come citare
Fascicolo
Sezione
Licenza
Copyright (c) 2025 Francesca Latino, Maria Giovanna Tafuri, Francesco Tafuri

Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 4.0 Internazionale.