Abstract
Background and objectivesGait can be severely affected by pain, muscle weakness, and aging resulting in lameness. Despite the high incidence of lameness, there are no studies on the features that are useful for classifying lameness patterns. Therefore, we aimed to identify features of high importance for classifying population differences in lameness patterns using an inertial measurement unit mounted above the sacral region.MethodsFeatures computed exhaustively for multidimensional time series consisting of three-axis angular velocities and three-axis acceleration were carefully selected using the Benjamini–Yekutieli procedure, and multiclass classification was performed using LightGBM (Microsoft Corp., Redmond, WA, USA). We calculated the relative importance of the features that contributed to the classification task in machine learning.ResultsThe most important feature was found to be the absolute value of the Fourier coefficients of the second frequency calculated by the one-dimensional discrete Fourier transform for real input. This was determined by the fast Fourier transformation algorithm using data of a single gait cycle of the yaw angular velocity of the pelvic region.ConclusionsUsing an inertial measurement unit worn over the sacral region, we determined a set of features of high importance for classifying differences in lameness patterns based on different factors. This completely new set of indicators can be used to advance the understanding of lameness.
Highlights
Gait is a complex movement, in which the body moves forward using the friction between the sole and the ground as support, and the centroid position moves up, down, left, and right
We aimed to identify features of high importance for classifying population differences in lameness patterns using an inertial measurement unit mounted above the sacral region
The most important feature was found to be the absolute value of the Fourier coefficients of the second frequency calculated by the one-dimensional discrete Fourier transform for real input
Summary
Gait is a complex movement, in which the body moves forward using the friction between the sole and the ground as support, and the centroid position moves up, down, left, and right. It is one of the most basic human movements. Several studies have shown that wearable inertial sensors can be used to classify gait patterns [2,3,4,5]. We aimed to identify features of high importance for classifying population differences in lameness patterns using an inertial measurement unit mounted above the sacral region
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