在认知任务期间对人类运动的多分形多尺度分析
Andrea Faini1,2, Laurent M Arsac3, Veronique Deschodt-Arsac3
1Department of Cardiovascular, Neural and Metabolic Sciences, Istituto Auxologico Italiano, IRCCS, 20149 Milan, Italy.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
多尺度多分体分析 (MFMS-DFA) 能够有效地检测认知负载对人类运动动态的影响. 协作游戏显著减少了对踏板控制的认知负载影响,突出了MFMS-DFA.
科学领域:
- 运动科学 运动科学
- 复杂系统分析 复杂系统分析
- 认知神经科学 认知神经科学
背景情况:
- 人类的运动适应依赖于多元化,一个复杂的碎形过程.
- 多分体光谱分析可以揭示感知,认知和行动中的适应过程.
- 基于DFA (MFMS-DFA) 的多尺度多分体分析是为心血管动态开发的.
研究的目的:
- 评估MFMS-DFA在人类运动动态中识别多尺度结构的实用性.
- 为了研究认知需求,特别是玩Tetris,如何影响运动多元化.
- 探索技能,认知需求和运动控制之间的相互作用.
主要方法:
- 36名参与者在三个条件下踩踏:参考骑自行车,单独骑自行车玩Tetris,以及协作骑自行车.
- 使用MFMS-DFA分析了踏板旋转周期 (PRP) 系列.
- 结果与线性化替代体进行了比较,以确认多分体性.
主要成果:
- 在各种尺度上,MFMS-DFA证实了踏板运动动态的多元化.
- 在大约16秒和64秒的尺度上,玩Tetris显著改变了多重分体性.
- 协作Tetris游戏减轻了这些变化,特别是在熟练的玩家中.
结论:
- MFMS-DFA对人类运动动态中的认知需求敏感.
- 该方法可以评估技能和任务需求之间的相互作用.
- MFMS-DFA显示出研究大脑运动协调和诊断神经系统疾病的潜力.
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