Gait Analysis by Causal Decomposition

Abstract

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.

Fusion of Dual Sensor Features for Fall Risk Assessment with Improved Attention Mechanism

“…This system is highly valuable for personal foot care, gait analysis, and clinical diagnosis. Peng et al [23] used an integrated empirical modal decomposition to decompose Parkinsonist left and right VGRF signals from high to low frequency bands into intrinsic modal functions. A person's gait health is then analyzed using their instantaneous phase dependence to assess the strength of the causal interaction between each intrinsic mode function.…”

“…By reviewing the literature [19][20][21][22][23][24][25][26][27][28][29][30][31], it is evident that the number of participants in fall risk assessment experiments typically ranges from 5 to 30 individuals. There are variations in the types and quantities of sensors used by different researchers, but the emphasis is predominantly on sensors located on the lower limbs.…”

Enhancing Fall Risk Assessment in the Elderly: A Study Utilizing Transfer Learning in an Improved EfficientNet Network with the Gramian Angular Field Technique

“…Wearable sensors, especially plantar pressure sensors, offer an innovative solution, unencumbered by environmental constraints and boasting substantial concealment, making them ideal for continuous motion monitoring of the elderly. Notable contributions in this field include the system leveraging plantar pressure sensors of Yu et al (2020) for inversion detection, and the wearable plantar pressure measurement and analysis system (WPPFMA) of Zhao et al (2020), and Peng et al (2021) use of plantar pressure signals in gait health analysis. Such studies highlight the significant potential of plantar pressure data in gait analysis, though its application in fall risk assessment remains underexplored.…”