强力板:用于处理原始强力板时间序列数据的R包
Raphael Hartmann1, Anton Koger2, Elisa R Straub3
1University of Freiburg, Freiburg, Germany. raphael.hartmann@psychologie.uni-freiburg.de.
Behavior research methods
|June 3, 2025
概括
本研究引入了与事件相关的方法和R包,以分析认知任务如何使用力板数据影响平衡控制. 这种方法增强了对精细时间尺度上的认知运动相互作用的分析.
科学领域:
- 认知神经科学 认知神经科学
- 发动机控制器的控制器
- 生物机械工程 生物机械工程
背景情况:
- 越来越多的证据将认知和运动过程联系起来.
- 传统的平衡分析方法汇总数据,掩盖了短期的认知效应.
- 辨别特定认知任务对平衡控制的影响需要更精细的时间分辨率.
研究的目的:
- 引入一种新的与事件相关的方法来分析认知任务对平衡控制的影响.
- 开发一个R-package (强力板) 来处理时间序列强力板数据.
- 在认知实验中建立处理力板数据的标准.
主要方法:
- 使用与事件相关的方法与力板测量.
- 开发了用于数据细分,过和基线校正的"强制板"R包.
- 在实验事件周围的用户定义的时间段内计算描述性统计.
主要成果:
- "强力板"R-包允许详细分析平衡控制动态.
- 与事件相关的方法允许在小时间尺度上调查对平衡的认知影响.
- 处理的数据适用于进一步的统计分析,如ANOVA.
结论:
- 拟议的与事件相关的方法和R组件有助于精确分析认知运动相互作用.
- 这种方法提高了在认知背景下平衡控制研究的可比性.
- 提供了一个标准化的框架,用于分析认知任务期间细粒度平衡调整.
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