一项使用社交网络分析技术的范围审查,总结了用于获取体育运动中运动员生存数据的方法的优势

P. J. Watson, J. Fieldsend, V. H. Stiles
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引用次数: 1

摘要

摘要为了帮助实施与后勤环境相关的运动员监控系统,需要易于访问的信息,总结在实践中使用数据采集方法来监控运动员的程度。在这篇范围界定综述中,社会网络分析和挖掘(SNAM)技术被用于总结和确定用于监测研究团队、个人、,基于场地和球场的运动(357篇文章;SPORTDiscus、MEDLINE、CINHAL和WebOfScience;2014-2018股份有限公司)。团队和基于场地的运动中最流行的组合是HR和/或sRPE(内部)和GPS,而在个人和基于球场的运动中,内部方法(如HR和sRPE)最流行。在以球场为基础的体育运动中,偶尔会将外部方法与内部数据采集方法相结合,使用加速度计或惯性测量单元(ACC/IMU)最为普遍。虽然对个人和球场运动的研究较少,但这份基于SNAM的总结表明,球场运动可能在使用ACC/IMU监测运动员方面处于领先地位。问卷调查和自我报告的数据获取方法在所有类别的体育运动中都很常见。这项范围界定审查为教练、体育科学家和研究人员提供了一个数据驱动的视觉资源,以帮助选择从与后勤环境相关的所有运动类别的运动员那里获取数据的方法。还介绍了如何根据本文提供的视觉摘要实际实施监控系统的指南。
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A scoping review using social network analysis techniques to summarise the prevalance of methods used to acquire data for athlete survelliance in sport
Abstract To aid the implementation of athlete surveillance systems relative to logistical circumstances, easy-to-access information that summarises the extent to which methods of acquiring data are used in practice to monitor athletes is required. In this scoping review, Social Network Analysis and Mining (SNAM) techniques were used to summarise and identify the most prevalent combinations of methods used to monitor athletes in research studying team, individual, field- and court-based sports (357 articles; SPORTDiscus, MEDLINE, CINHAL, and WebOfScience; 2014-2018 inc.) . The most prevalent combination in team and field-based sports were HR and/or sRPE (internal) and GPS, whereas in individual and court-based sports, internal methods (e.g., HR and sRPE) were most prevalent. In court-based sports, where external methods were occasionally collected in combination with internal methods of acquiring data, the use of accelerometers or inertial measuring units (ACC/IMU) were most prevalent. Whilst individual and court-based sports are less researched, this SNAM-based summary reveals that court-based sports may lead the way in using ACC/IMU to monitor athletes. Questionnaires and self-reported methods of acquiring data are common in all categories of sport. This scoping review provides coaches, sport-scientists and researchers with a data-driven visual resource to aid the selection of methods of acquiring data from athletes in all categories of sport relative to logistical circumstances. A guide on how to practically implement a surveillance system based on the visual summaries provided herein, is also presented.
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来源期刊
International Journal of Computer Science in Sport
International Journal of Computer Science in Sport Computer Science-Computer Science (all)
CiteScore
2.20
自引率
0.00%
发文量
4
审稿时长
12 weeks
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