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

Satoshi Arita1, Daisuke Nishiyama1, Takaya Taniguchi1

  • 1Department of Orthopedic Surgery, Wakayama Medical University, Wakayama, Japan.

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Summary

Researchers identified key features for classifying lameness patterns using an inertial measurement unit. The absolute value of Fourier coefficients of the second frequency in pelvic yaw angular velocity proved most important for gait analysis.

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Area of Science:

  • Biomechanics
  • Gait Analysis
  • Wearable Technology

Background:

  • Lameness significantly impacts gait due to pain, muscle weakness, and aging.
  • High incidence of lameness lacks studies on classifying its patterns.
  • Need for objective methods to identify lameness features.

Purpose of the Study:

  • Identify high-importance features for classifying lameness patterns.
  • Utilize an inertial measurement unit (IMU) for gait analysis.
  • Differentiate lameness patterns based on various factors.

Main Methods:

  • Computed features from multidimensional time series (angular velocity, acceleration).
  • Applied Benjamini-Yekutieli procedure for feature selection.
  • Performed multiclass classification using LightGBM machine learning model.

Main Results:

  • Identified the absolute value of Fourier coefficients of the second frequency as the most important feature.
  • Feature derived from one-dimensional discrete Fourier transform of pelvic yaw angular velocity.
  • Fast Fourier Transform algorithm used for single gait cycle analysis.

Conclusions:

  • Developed a novel set of indicators using IMU data from the sacral region.
  • These indicators are crucial for classifying differences in lameness patterns.
  • Findings advance the understanding and objective assessment of lameness.