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Classifying sitting, standing, and walking using plantar force data

Medical & Biological Engineering & Computing volume 59, pages 257–270 (2021)Cite this article

Abstract

Prolonged static weight-bearing at work may increase the risk of developing plantar fasciitis (PF). However, to establish a causal relationship between weight-bearing and PF, a low-cost objective measure of workplace behaviors is needed. This proof-of-concept study assesses the classification accuracy and sensitivity of low-resolution plantar pressure measurements in distinguishing workplace postures. Plantar pressure was measured using an in-shoe measurement system in eight healthy participants while sitting, standing, and walking. Data was resampled to simulate on/off characteristics of 24 plantar force sensitive resistors. The top 10 sensors were evaluated using leave-one-out cross-validation with machine learning algorithms: support vector machines (SVMs), decision tree (DT), discriminant analysis (DA), and k-nearest neighbors (KNN). SVM and DT best classified sitting, standing, and walking. High classification accuracy was obtained with five sensors (98.6% and 99.1% accuracy, respectively) and even a single sensor (98.4% and 98.4%, respectively). The central forefoot and the medial and lateral midfoot were the most important classification sensor locations. On/off plantar pressure measurements in the midfoot and central forefoot can accurately classify workplace postures. These results provide the foundation for a low-cost objective tool to classify and quantify sedentary workplace postures.

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Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) EGP 491213-15 and the Simon Fraser University Community Trust Endowment Fund.

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Authors and Affiliations

  1. School of Mechatronic Systems Engineering, Simon Fraser University, 250-13450 102 Ave, Surrey, BC, V3T 0A3, Canada

    Kohle J. Merry, Evan Macdonald, Omar Aziz, Edward Park & Carolyn J. Sparrey

  2. School of Health and Exercise Sciences, University of British Columbia, Kelowna, BC, Canada

    Megan MacPherson

  3. Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada

    Michael Ryan

  4. Kintec Footlabs Inc., Surrey, BC, Canada

    Michael Ryan

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  1. Kohle J. Merry
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  2. Evan Macdonald
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Correspondence to Kohle J. Merry.

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Michael Ryan is a salaried employee of Kintec Footlabs Inc.

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Merry, K.J., Macdonald, E., MacPherson, M. et al. Classifying sitting, standing, and walking using plantar force data. Med Biol Eng Comput 59, 257–270 (2021). https://doi.org/10.1007/s11517-020-02297-4

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  • DOI: https://doi.org/10.1007/s11517-020-02297-4

Keywords

  • Pedobarography
  • Pattern recognition
  • Posture differentiation
  • Plantar fasciitis
  • Weight-bearing