通过智能座椅了解网络运动员的行为：CS：GO和Monolith团队场景
Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario
摘要：
电子竞技是一个快速发展的多学科领域。然而，电子竞技的研究和实验还处于起步阶段。在这项工作中，我们提出了一个智能椅子平台-一种不引人注目的方法来收集电子竞技运动员的数据，并使用机器学习方法进一步处理数据。用例场景涉及三组玩家：“网络运动员”（Monolith团队）、半职业玩家和新手，他们都在玩CS：GO规则。特别是，我们从集成在椅子上的加速度计和陀螺仪收集数据，并应用机器学习算法进行数据分析。我们的研究结果表明，职业运动员可以通过他们在玩游戏时在椅子上的行为来识别。
英文摘要：
eSports is the rapidly developing multidisciplinary domain. However, research and experimentation in eSports are in the infancy. In this work, we propose a smart chair platform - an unobtrusive approach to the collection of data on the eSports athletes and data further processing with machine learning methods. The use case scenario involves three groups of players: `cyber athletes' (Monolith team), semi-professional players and newbies all playing CS:GO discipline. In particular, we collect data from the accelerometer and gyroscope integrated in the chair and apply machine learning algorithms for the data analysis. Our results demonstrate that the professional athletes can be identified by their behaviour on the chair while playing the game.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
