Abstract
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.
Highlights
Only D1 and the discrete wavelet transform (DWT)-based features from the intermediate and low frequency bands of vertical ground reaction force (VGRF) signals can reveal the progression of Parkinson’s disease (PD) patients
It is worth noting that the purpose of this study is not to prove that our proposed approach is omnipotent for gait analysis, but to show that: (1) the study of bilateral gait causality in frequency domain has physiological significance; (2) diseases may lead to changes in gait causality, which has not been found in previous studies
Our study applied a new method for performing gait analysis, namely causal decomposition, which is based on analyzing the causal interaction of bilateral kinetic signals at different frequency bands
Summary
A. Walking Mechanisms and Quantitative Gait Analysis. It should be noted that if there is a problem in any one of these physiological components, it may produce an abnormal gait in the individual. Quantitative analysis of individual gait patterns has become an important way to understand the mechanisms of walking and diseases related to it. Quantitative gait analysis usually involves the study of an individual’s walking patterns through the collection and analysis of the person’s physiological, kinetic, and kinematic signals, such as their electromyographic signals, vertical ground reaction force (VGRF), and acceleration signals, and these have been broadly applied in many different fields to date. Quantitative gait analysis based on kinetic (or kinematic) signals helps clinicians to diagnose these diseases, but can be used as a standard tool to evaluate the rehabilitative effect of their treatment. It can be seen that quantitative gait analysis plays an increasingly important role in the fields of medicine and engineering
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