Application of Internet of Things Data Mining in Sports Teaching and Training System Optimization
Year: 2025
Type: article
Abstract: In the digital era, the integration of advanced technologies such as the Internet of Things (IoT), data mining, and Deep Learning (DL) has significantly transformed various sectors, including education and sports. Traditional sports teaching methods, such as manual observation, video analysis, verbal feedback, and repetitive drills, largely rely on manual observation and subjective assessment, which often result in inconsistencies and limited feedback. This research proposes a novel sports teaching and training system that combines IoT-based data mining with machine learning techniques to enhance the effectiveness, precision, and intelligence of Physical Education (PE). With the growing demand for personalized and data-driven training approaches, IoT devices such as wearable sensors, smart garments, and motion trackers are employed to continuously collect real-time physiological and motion-related data from students and athletes during training sessions. The collected data undergo preprocessing through data mining techniques aimed at eliminating noise and normalization to identify behavioral patterns critical to training optimization. Spatial features are extracted and refined using Power Spectral Density (PSD) analysis. To enhance prediction accuracy and model scalability, an Artificial Gorilla Troops Optimizer-driven Scalable Extreme Learning Machine (AGTO-SELM) is introduced for efficient classification and forecasting of training outcomes. The proposed system delivers adaptive feedback and personalized training recommendations, enabling coaches and educators to make informed decisions in real time and closely monitor progress. To assess its performance and practical utility, the model is evaluated through experimental simulations in actual sports teaching environments. The findings reveal that the proposed model achieves a substantial accuracy improvement of 97.4%, along with higher specificity, sensitivity, and F1-score. This research underscores the transformative potential of intelligent technologies in evolving traditional physical education into a smart, adaptive, and data-informed learning experience.
Author: Lu Chen
Institution: Tianjin University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411903223