跨步变化的多重分形性表明,步行比跑步涉及更多的运动调整和调整。

Frontiers in network physiology Pub Date : 2023-10-19 eCollection Date: 2023-01-01 DOI:10.3389/fnetp.2023.1294545
Taylor J Wilson, Madhur Mangalam, Nick Stergiou, Aaron D Likens
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摘要

引言:看似周期性的人类步态在适应不断变化的任务约束时,会表现出步幅之间的变化。最佳运动变异性假说(OMVH)指出,健康的步幅变化表现出“分形”——连续步幅中的一种特定时间结构,它是有序的、稳定的,但也是可变的,并且具有适应性。先前的研究主要集中在一个单一的分形测度上,即“单分形”。然而,这个测度可能随时间而变化;跨步到跨步的变化可以表现出“多重分形”。跨步到跨步变化中更大的多重分形将突出调整和调整动作的能力。方法:我们在跑步机和地上进行的自定步步行和跑步试验中,对8名健康成年人的单分形和多重分形进行了研究。跌倒数据是通过放置在他们脚跟和脚上的力敏传感器收集的。除了步长时间序列的多重分形谱宽度W和多重分形谱中的不对称性WAsym外,我们还研究了自行步行与跑步、跑步机与地上运动对单分形α-DFA测量的影响。结果:虽然在几乎所有条件下α-DFA都大于0.50,但在地上跑步和运动时α-DFA高于在跑步机上行走和运动。同样,地上运动时的W比在跑步机上运动时大,但相反的趋势表明,走路时的W大于跑步时的W。负方向上较大的WAsym值表明,与跑步相比,步行在短步幅间隔的持续性方面表现出更多的变化。然而,跑步机和地上运动的调节和调整能力没有差异,尽管两者在短步幅间隔的持续性方面都表现出更多的差异。讨论:因此,与跑步相比,短步幅间隔比长步幅间隔的异质性越大,步行的多重分形越大,WAsym负值越大。我们的研究结果强调了结合多重分形方法来测试OMVH预测的必要性。
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Multifractality in stride-to-stride variations reveals that walking involves more movement tuning and adjusting than running.

Introduction: The seemingly periodic human gait exhibits stride-to-stride variations as it adapts to the changing task constraints. The optimal movement variability hypothesis (OMVH) states that healthy stride-to-stride variations exhibit "fractality"-a specific temporal structure in consecutive strides that are ordered, stable but also variable, and adaptable. Previous research has primarily focused on a single fractality measure, "monofractality." However, this measure can vary across time; strideto-stride variations can show "multifractality." Greater multifractality in stride-tostride variations would highlight the ability to tune and adjust movements more. Methods: We investigated monofractality and multifractality in a cohort of eight healthy adults during self-paced walking and running trials, both on a treadmill and overground. Footfall data were collected through force-sensitive sensors positioned on their heels and feet. We examined the effects of self-paced walking vs. running and treadmill vs. overground locomotion on the measure of monofractality, α-DFA, in addition to the multifractal spectrum width, W, and the asymmetry in the multifractal spectrum, WAsym, of stride interval time series. Results: While the α-DFA was larger than 0.50 for almost all conditions, α-DFA was higher in running and locomoting overground than walking and locomoting on a treadmill. Similarly, W was greater while locomoting overground than on a treadmill, but an opposite trend indicated that W was greater in walking than running. Larger WAsym values in the negative direction suggest that walking exhibits more variation in the persistence of shorter stride intervals than running. However, the ability to tune and adjust movements does not differ between treadmill and overground, although both exhibit more variation in the persistence of shorter stride intervals. Discussion: Hence, greater heterogeneity in shorter than longer stride intervals contributed to greater multifractality in walking compared to running, indicated by larger negative WAsym values. Our results highlight the need to incorporate multifractal methods to test the predictions of the OMVH.

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