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精英体育中的人工智能——成功案例与挑战的叙述性综述

Artificial Intelligence in Elite Sports-A Narrative Review of Success Stories and Challenges.

作者信息

Hammes Fabian, Hagg Alexander, Asteroth Alexander, Link Daniel

机构信息

Chair of Performance Analysis and Sports Informatics, Department of Sport and Health Science, Technical University of Munich, Munich, Germany.

Computer Science, Institute of Technology, Resource and Energy-Efficient Engineering, Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin, Germany.

出版信息

Front Sports Act Living. 2022 Jul 11;4:861466. doi: 10.3389/fspor.2022.861466. eCollection 2022.

Abstract

This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.

摘要

本文探讨了人工智能(AI)在精英体育中的作用。我们从两个角度来探讨这个话题。首先,我们基于文献综述了人工智能在体育以外领域的成功案例。我们在机器感知、机器学习与建模、规划与优化以及交互与干预等领域确定了多种方法,这些方法具有改善训练和比赛的潜力。其次,我们了解了人工智能在精英体育中的应用现状。因此,除了另一篇文献综述外,我们还采访了与各自国家主要精英体育服务机构密切相关的顶尖体育科学家。对这篇文献综述和访谈的分析表明,最活跃的领域是信号和图像处理方法类别。然而,在过去几年中,建模与规划领域的项目越来越受欢迎。基于这两个角度,我们提取了不足、问题和机遇,并将它们总结为体育分析界面临的六个关键挑战。这些挑战包括数据收集、从业者对人工智能的可控性以及人工智能结果的可解释性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c472/9309390/d7c60ed8b949/fspor-04-861466-g0001.jpg

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