Template-Type: ReDIF-Article 1.0
Title: ARTIFICIAL INTELLIGENCE-DRIVEN PERSONALIZED TRAINING SYSTEMS AND THEIR IMPACT ON ATHLETIC PERFORMANCE, INJURY PREVENTION, AND PHYSICAL EDUCATION OUTCOMES: A SYSTEMATIC EMPIRICAL STUDY
Author-Name: Chuliyev Yodgor Tolib o'g'li
Author-Email: yodgorchuliyev1@gmail.com
Author-Workplace-Name: University of Economy and Pedagogy
Abstract: Background: Traditional athletic training and physical education frameworks frequently employ standardized methodologies that fail to accommodate individual biomechanical variations, track real-time physiological strain, or scale effectively in educational environments. Objective: This study evaluates the efficacy of an Artificial Intelligence-Driven Personalized Training System (AI-PTS) that integrates computer vision, multi-sensor wearable fusion, and predictive analytics to optimize athletic performance, reduce injury incidence, and enhance student engagement in physical education. Methods: A 24-week randomized controlled trial was conducted with 180 participants, comprising elite collegiate athletes (n = 90) and university physical education students (n = 90). Participants were randomized into an Experimental Group (AI-PTS intervention) and a Control Group (traditional training). The AI-PTS utilized Convolutional Neural Networks (CNNs) for kinematic motion capture, Long Short-Term Memory (LSTM) networks for wearable data fusion, and Extreme Gradient Boosting (XGBoost) for injury risk forecasting. Statistical analysis featured mixed-design ANOVA, multiple linear regression, and Structural Equation Modeling (SEM). Results: The experimental athletic cohort exhibited statistically significant improvements in VO2 max (+14.2%, p < .001, partial eta squared = .28) and sport-specific power output (+18.5%, p < .001). The predictive injury model achieved an AUC-ROC of 0.91, driving a 42.3% reduction in overuse injuries (p < .01). Within the physical education cohort, SEM demonstrated that AI-driven personalized feedback directly enhanced intrinsic motivation (β = .48, p < .001) and motor skill acquisition (β= .52, p < .001). Conclusion: The integration of multimodal AI systems into training and pedagogy provides a scalable paradigm for optimizing human performance and mitigating musculoskeletal injuries, bridging the gap between elite sports analytics and physical education.
Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Sports Analytics, Injury Prediction, Wearable Technologies, Physical Education.
Creation-Date: 2026-05-01
Journal: Synoptic: International Journal of Multidisciplinary Research
Volume: 2
Issue: 1
Pages: 52-62
File-URL: https://lmhqneinkzpxfhahvcvx.supabase.co/storage/v1/object/public/pdfs/articles/1780393224235-52-62.pdf
File-Format: Application/pdf
Handle: RePEc:snp:journl:art-1780393226135