Recent studies have investigated bilateral gaits based on the causality analysis of kinetic (or kinematic) signals recorded using both feet. However, these approaches have not considered the influence of their simultaneous causation, which might lead to inaccurate causality inference. Furthermore, the causal interaction of these signals has not been investigated within their frequency domain. Therefore, in this study we attempted to employ a causal-decomposition approach to analyze bilateral gait. The vertical ground reaction force (VGRF) signals of Parkinson's disease (PD) patients and healthy control (HC) individuals were taken as an example to illustrate this method. To achieve this, we used ensemble empirical mode decomposition to decompose the left and right VGRF signals into intrinsic mode functions (IMFs) from the high to low frequency bands. The causal interaction strength (CIS) between each pair of IMFs was then assessed through the use of their instantaneous phase dependency. The results show that the CISes between pairwise IMFs decomposed in the high frequency band of VGRF signals can not only markedly distinguish PD patients from HC individuals, but also found a significant correlation with disease progression, while other pairwise IMFs were not able to produce this. In sum, we found for the first time that the frequency specific causality of bilateral gait may reflect the health status and disease progression of individuals. This finding may help to understand the underlying mechanisms of walking and walking-related diseases, and offer broad applications in the fields of medicine and engineering.

中文翻译：

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通过因果分解进行步态分析


最近的研究基于双脚记录的动力学（或运动学）信号的因果关系分析研究了双侧步态。然而，这些方法没有考虑它们同时因果关系的影响，这可能导致因果关系推断不准确。此外，这些信号的因果相互作用尚未在其频域内进行研究。因此，在本研究中，我们尝试采用因果分解方法来分析双边步态。以帕金森病（PD）患者和健康对照（HC）个体的垂直地面反作用力（VGRF）信号为例来说明该方法。为了实现这一目标，我们使用集成经验模态分解将左右 VGRF 信号从高频段到低频段分解为固有模态函数 (IMF)。然后通过使用瞬时相位依赖性来评估每对 IMF 之间的因果相互作用强度 (CIS)。结果表明，在 VGRF 信号高频段分解的成对 IMF 之间的 CIS 不仅可以显着区分 PD 患者和 HC 个体，而且还发现与疾病进展存在显着相关性，而其他成对 IMF 无法产生这一点。总之，我们首次发现双侧步态的频率特异性因果关系可能反映个体的健康状况和疾病进展。这一发现可能有助于了解步行和步行相关疾病的潜在机制，并在医学和工程领域提供广泛的应用。