计算机科学
数据科学
分析
人工智能
开放式研究
领域(数学分析)
透视图(图形)
大数据
机器学习
新兴技术
分类
即兴的
可穿戴计算机
人机交互
万维网
数据挖掘
数学分析
数学
程序设计语言
嵌入式系统
作者
Indrajeet Ghosh,Sreenivasan Ramasamy Ramamurthy,Avijoy Chakma,Nirmalya Roy
摘要
Abstract The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors , computer vision , and wireless and mobile‐based applications . Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics. This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things
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