Feature selection to classify lameness using a smartphone-based inertial measurement unit.

ABSTRACT:

Background and objectives

Gait 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.

Methods

Features 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 feat

SUBMITTER: Arita S 

PROVIDER: S-EPMC8483374 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature