超越边界:一个基于位置的工具包,用于在不同的环境中量化群体动态.
Seth Elkin-Frankston1,2, James McIntyre3, Tad T Brunyé4,3
1Cognitive Science Branch, US Army DEVCOM Soldier Center, Natick, MA, USA. seth.elkin-frankston.civ@army.mil.
Cognitive research: principles and implications
|February 21, 2025
概括
研究人员开发了一套新的工具包,用于分析在开放环境中的人类群体运动. 该工具通过整合认知因素来量化集体动态,并预测团队表现.
科学领域:
- 运动生态学 运动生态学
- 集体行为分析 集体行为分析
- 人与动物的交互研究研究.
背景情况:
- 现有的运动分析工具仅限于受界环境或动物生态.
- 在分析大规模,无边界或半受约束环境中的人类群体动态方面存在差距.
- 旅游,自行车,搜救和军事行动等领域缺乏专门的分析工具.
研究的目的:
- 调查现有的方法来描述人类和动物的个人和集体运动.
- 开发一个通用的工具包,用于量化在开放环境中的人类群体动态.
- 将决策和情境意识等认知因素整合到运动分析中.
主要方法:
- 对现有的运动分析方法和指标的调查.
- 开发基于Python的工具包,用于提取和分析GPS位置数据.
- 该工具包应用于步行期间从军事人员获得的GPS数据.
主要成果:
- 建立了一个全面的工具包,用于量化目标导向运动中的群体动态.
- 该工具包将基于位置的指标与认知因素 (决策,情境意识,协调) 结合起来.
- 一个用例展示了使用衍生指标预测后续实践中的组绩效.
结论:
- 开发的工具包将基于位置的分析扩展到新的领域,增强对集体运动的理解.
- 认知过程在开放环境中显著影响群体行为和新兴特性.
- 该工具包为评估和提高团队在各种运营环境中的有效性提供了实际实用性.
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