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Defining Groupings and Classification of Human Gait Using Correlation of Ground Reaction Force Measurements

  • Ellis Kessler
  • Pablo A. Tarazaga
  • Robin Queen
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

Classification of a person’s gait through quantitative methods has wide reaching applications in security, marketing, and healthcare. This study uses ground reaction force (GRF) measurements of healthy subjects and patients with osteoarthritis (OA) to define groupings and classify subjects. In addition to grouping classes into known qualities (gender, diagnosis, etc.), this work introduces classes based on groups coming directly from correlating GRFs from different subjects. Using correlation allows new groupings to be established and more accurate classification results because continuous force time histories are compared as opposed to conventional discrete force data (i.e. peaks). Two new classes are introduced from the data which can be classified with a 92% accuracy, and the physical meaning of these classes is investigated. Comparison of a single healthy person’s walking pattern to multiple classes builds a signature that could be used to identify specific individuals. Additionally, patients suffering from OA do not correlate well with healthy groupings and can be distinguished from healthy subjects. This allows for the possibility of using GRFs to track patients’ rehabilitation. It is expected that as a patient progresses through a rehabilitation program and begins to recover, their walking patterns will become more consistent and be more highly correlated with healthy groupings.

Keywords

Gait Classification Correlation Grouping GRF 

Notes

Acknowledgements

Dr. Tarazaga would like to acknowledge the financial support of the John R. Jones Faculty Fellowship.

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Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  • Ellis Kessler
    • 1
  • Pablo A. Tarazaga
    • 1
  • Robin Queen
    • 1
  1. 1.Department of Mechanical Engineering, Virginia Tech Smart Infrastructure Lab (VTSIL)BlacksburgUSA

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