Exploring the Integration of Artificial Intelligence in Sports Coaching: Enhancing Training Efficiency, Injury Prevention, and Overcoming Implementation Barriers  

作　　者：Zeqi Huang Wenjun Wang Zixuan Jia Ziqi Wang Zeqi Huang;Wenjun Wang;Zixuan Jia;Ziqi Wang(Graduate School, Shandong Sport University, Jinan, China;Department of General Education, School of Sports and Art, Shandong First Medical University, Taian, China)

机构地区：[1]Graduate School, Shandong Sport University, Jinan, China [2]Department of General Education, School of Sports and Art, Shandong First Medical University, Taian, China

出　　处：《Journal of Computer and Communications》2024年第12期201-217,共17页电脑和通信(英文)

摘　　要：Artificial Intelligence (AI) has emerged as a transformative force across industries, with notable applications in sports coaching. Its capabilities, ranging from machine learning to real-time feedback systems, enable coaches to process complex data and enhance decision-making. This study investigates the integration of AI into sports coaching, emphasizing its potential to revolutionize training efficiency, optimize athlete performance, and reduce injury risks. However, AI implementation still faces challenges such as technical complexity, budgetary constraints, data privacy and ethical considerations, and psychological resistance. Research Objective: The purpose of this study is to explore the efficient use of AI in sport coaching, to propose strategies to address the challenges encountered in implementation, and to provide implementation guidelines for sport organizations. Research Methodology: This paper provides an in-depth analysis of the application of AI in data analysis and training adjustments, injury prediction, and personalized training regimens through literature review and case studies, and proposes effective implementation strategies in combination with actual cases. Findings: The study found that AI technology has significant advantages in enhancing training science and effectiveness, such as real-time data analysis tools and machine learning models that can improve training personalization and science, and AI-driven biomechanical analysis and injury prediction systems that can help to reduce the risk of injuries and improve safety for athletes. However, technical complexity, financial pressures, and data privacy concerns limit their use in small and medium-sized organizations. Research Conclusion: Successful implementation of AI technology in sports coaching requires a multi-layered strategy that includes ongoing training, strategic partnerships, and phased introduction. Future research should focus on combining with emerging technologies such as virtual reality and augmented reality, as well as eArtificial Intelligence (AI) has emerged as a transformative force across industries, with notable applications in sports coaching. Its capabilities, ranging from machine learning to real-time feedback systems, enable coaches to process complex data and enhance decision-making. This study investigates the integration of AI into sports coaching, emphasizing its potential to revolutionize training efficiency, optimize athlete performance, and reduce injury risks. However, AI implementation still faces challenges such as technical complexity, budgetary constraints, data privacy and ethical considerations, and psychological resistance. Research Objective: The purpose of this study is to explore the efficient use of AI in sport coaching, to propose strategies to address the challenges encountered in implementation, and to provide implementation guidelines for sport organizations. Research Methodology: This paper provides an in-depth analysis of the application of AI in data analysis and training adjustments, injury prediction, and personalized training regimens through literature review and case studies, and proposes effective implementation strategies in combination with actual cases. Findings: The study found that AI technology has significant advantages in enhancing training science and effectiveness, such as real-time data analysis tools and machine learning models that can improve training personalization and science, and AI-driven biomechanical analysis and injury prediction systems that can help to reduce the risk of injuries and improve safety for athletes. However, technical complexity, financial pressures, and data privacy concerns limit their use in small and medium-sized organizations. Research Conclusion: Successful implementation of AI technology in sports coaching requires a multi-layered strategy that includes ongoing training, strategic partnerships, and phased introduction. Future research should focus on combining with emerging technologies such as virtual reality and augmented reality, as well as e

关 键 词：Sports Coaching Artificial Intelligence Training Optimization 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]