利用虚拟现实技术优化高水平运动员预测技能的跨学科框架

Gilles Montagne, Nicolas Mascret, Martin Bossard, Loïc Chomienne, Simon Ledouit, Guillaume Rao, Nicolas Tordi, Eulalie Verhulst, Richard Kulpa
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引用次数: 0

摘要

我们的目标是展示跨学科框架如何为部署创新的虚拟现实训练课程铺平道路,以提高顶级运动员的预判能力。这种提高是如此具有挑战性,以至于有些作者说这就像是 "为不可能的事情进行训练"。这个框架目前正在实施,是为 2024 年巴黎奥运会运动员做准备的一个项目的一部分,它以生态动力学方法为基础,具有跨学科的创新性,但最重要的是,它克服了该领域中更多传统训练方法的局限性,旨在优化顶级运动员的预测技能。其目标是应对从虚拟伙伴和对手的设计到虚拟现实训练计划的部署等一系列挑战,同时确保这种创新的虚拟现实训练方案的可接受性和认可度,并测量相关的工作量。
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An interdisciplinary framework to optimize the anticipation skills of high-level athletes using virtual reality
The ambition of our contribution is to show how an interdisciplinary framework can pave the way for the deployment of innovative virtual reality training sessions to improve anticipation skills in top-level athletes. This improvement is so challenging that some authors say it is like “training for the impossible”. This framework, currently being implemented as part of a project to prepare athletes for the 2024 Olympic Games in Paris, based on the ecological-dynamics approach to expertise, is innovative in its interdisciplinary nature, but also and above all because it overcomes the limitations of more traditional training methods in the field designed to optimize anticipation skills in top-level athletes. The ambition is to tackle successive challenges ranging from the design of virtual partners and opponents to the deployment of training programs in virtual reality, while ensuring the acceptability and acceptance of such innovative virtual reality training protocols and measuring associated workloads.
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