Application of Sports Equipment Image Intelligent Recognition Response APP in Sports Training and Teaching

Abstract

INTRODUCTION: The paper addresses the integration of intelligent technology in university physical education, highlighting the need for improved analysis methods for sports equipment image recognition apps to enhance teaching quality.

OBJECTIVES: The study aims to develop a more accurate and efficient APP use analysis method for sports equipment image recognition, utilizing intelligent optimization algorithms and kernel limit learning machines.

METHODS: The proposed method involves constructing an APP usage effect analysis index system, improving kernel limit learning machines through talent mining algorithms, and validating the model using user behavior data. The method integrates a talent mining algorithm to enhance the kernel limit learning machine (KELM). This integration aims to refine the learning machine’s ability to accurately analyze the large datasets generated by the APP's use, optimizing the parameters to improve prediction accuracy and processing speed.

RESULTS: Preliminary tests on the sports equipment image intelligent recognition response APP demonstrate improved accuracy and efficiency in analyzing the APP's usage effects in physical education settings. The study compares the performance of the TDA-KELM algorithm with other algorithms like ELM, KELM, GWO-KELM, SOA-KELM, and AOA-KELM. The TDA-KELM algorithm showed the smallest relative error of 0.025 and a minimal time of 0.0025, indicating higher accuracy and efficiency. The analysis highlighted that the TDA-KELM algorithm outperformed others in analyzing the usage effects of sports equipment image recognition apps, with lower errors and faster processing times.

CONCLUSION: The study successfully develops an enhanced APP use analysis method, showcasing potential for more accurate and real-time analysis in the application of sports equipment image recognition in physical education.