Gait analysis to classify external load conditions using linear discriminant analysis

Minhyung Lee, Michael Roan, Benjamin Smith, Thurmon E. Lockhart

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

There are many instances where it is desirable to determine, at a distance, whether a subject is carrying a hidden load. Automated detection systems based on gait analysis have been proposed to detect subjects that carry hidden loads. However, very little baseline gait kinematic analysis has been performed to determine the load carriage effect while ambulating with evenly distributed (front to back) loads on human gait. The work in this paper establishes, via high resolution motion capture trials, the baseline separability of load carriage conditions into loaded and unloaded categories using several standard lower body kinematic parameters. A total of 23 participants (19 for training and 4 for testing) were studied. Satisfactory classification of participants into the correct loading condition was achieved by employing linear discriminant analysis (LDA). Six lower body kinematic parameters including ranges of motion and path lengths from the phase portraits were used to train the LDA to discriminate loaded and unloaded walking conditions. Baseline performance from 4 participants who were not included in training data sets show that the use of LDA provides a 92.5% correct classification over two loaded and unloaded walking conditions. The results suggest that there are gait pattern changes due to external loads, and LDA could be applied successfully to classify the gait patterns with an unknown load condition.

Original languageEnglish (US)
Pages (from-to)226-235
Number of pages10
JournalHuman Movement Science
Volume28
Issue number2
DOIs
StatePublished - Apr 1 2009

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Keywords

  • External loads
  • Gait analysis
  • Linear discriminant analysis
  • Locomotion

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Experimental and Cognitive Psychology

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