Center for Research in Human Movement Variability, University of Nebraska at Omaha, 6160 University Drive South, Omaha, NE, 68182-0860, USA.
Department of Physical Performance, Norwegian School of Sport Sciences, Sognsveien 220, 0806, Oslo, Norway.
Ann Biomed Eng. 2021 Mar;49(3):979-990. doi: 10.1007/s10439-020-02616-8. Epub 2021 Feb 9.
The usage of entropy analysis in gait research has grown considerably the last two decades. The present paper reviews the application of different entropy analyses in gait research and provides recommendations for future studies. While single-scale entropy analysis such as approximate and sample entropy can be used to quantify regularity/predictability/probability, they do not capture the structural richness and component entanglement characterized by a complex system operating across multiple spatial and temporal scales. Thus, for quantification of complexity, either multiscale entropy or refined composite multiscale entropy is recommended. For both single- and multiscale-scale entropy analyses, care should be made when selecting the input parameters of tolerance window r, vector length m, time series length N and number of scales. This selection should be based on the proposed research question and the type of data collected and not copied from previous studies. Parameter consistency should be investigated and published along with the main results to ensure transparency and enable comparisons between studies. Furthermore, since the interpretation of the absolute size of both single- and multiscale entropy analyses outcomes is not straightforward, comparisons should always be made with a control condition or group.
过去二十年，熵分析在步态研究中的应用得到了极大的发展。本文回顾了不同熵分析在步态研究中的应用，并为未来的研究提供了建议。虽然单尺度熵分析（如近似熵和样本熵）可用于量化规律性/可预测性/概率，但它们无法捕捉复杂系统在多个时空尺度上运行的结构丰富性和成分纠缠性。因此，为了量化复杂性，建议使用多尺度熵或改进的复合多尺度熵。对于单尺度和多尺度熵分析，在选择容忍窗口 r、向量长度 m、时间序列长度 N 和尺度数等输入参数时应谨慎。这种选择应该基于提出的研究问题和所收集的数据类型，而不是从以前的研究中复制。应随着主要结果一起公布参数一致性，以确保透明度并能够在研究之间进行比较。此外，由于单尺度和多尺度熵分析结果的绝对大小的解释并不直接，因此应始终与对照条件或组进行比较。
