• 1

    Giacomozzi C, Keijsers N, Pataky TC, et al: International scientific consensus on medical plantar pressure measurement devices: technical requirements and performance. Ann Ist Super Sanità 48: 259, 2012.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Willems TM, De Clercq D, Delbaere K, et al: A prospective study of gait related risk factors for exercise-related lower leg pain. Gait Posture 23: 91, 2006.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Najafi B, Crews RT, Armstrong DG, et al: Can we predict outcome of surgical reconstruction of Charcot neuroarthropathy by dynamic plantar pressure assessment? a proof of concept study. Gait Posture 31: 87, 2010.

    • Crossref
    • PubMed
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 4

    Pataky TC, Maiwald C: Spatiotemporal volumetric analysis of dynamic plantar pressure data. Med Sci Sports Exerc 43: 1582, 2011.

    • Crossref
    • PubMed
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 5

    Pataky TC, Caravaggi P, Savage R, et al: New insights into the plantar pressure correlates of walking speed using pedobarographic statistical parametric mapping (pSPM). J Biomech 41: 1987, 2008.

    • Crossref
    • PubMed
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 6

    Keijsers NLW, Stolwijk NM, Nienhuis B, et al: A new method to normalize plantar pressure measurements for foot size and foot progression angle. J Biomech 5: 87, 2009.

    • Crossref
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 7

    Pataky TC, Goulermas JY: Pedobarographic statistical parametric mapping (pSPM): a pixel-level approach to foot pressure image analysis. J Biomech 41: 2136, 2009.

    • Crossref
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 8

    Keijsers NLW, Stolwijk NM, Louwerens JWK, et al: Classification of forefoot pain based on plantar pressure measurements. Clin Biomech (Bristol, Avon) 28: 350, 2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9

    Maris E, Oostenveld R: Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164: 177, 2007.

    • Crossref
    • PubMed
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 10

    Stolwijk NM, Duysens J, Louwerens JWK, et al: Flat feet, happy feet? comparison of the dynamic plantar pressure distribution and static medial foot geometry between Malawian and Dutch adults. PLoS One 8: e57209, 2013.

    • Crossref
    • PubMed
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 11

    Deschamps K, Matricali GA, Roosen P, et al: Classification of forefoot plantar pressure distribution in persons with diabetes: a novel perspective for the mechanical management of diabetic foot? PLoS One 8: e79924, 2013.

    • Crossref
    • PubMed
    • Web of Science
    • Search Google Scholar
    • Export Citation
  • 12

    Manal K, Stanhope SJ: A novel method for displaying gait and clinical movement analysis data. Gait Posture 20: 222, 2004.

  • 13

    Manal K, Chang C-C, Hamill J, et al: A three-dimensional data visualization technique for reporting movement pattern deviations. J Biomech 38: 2151, 2005.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    De Cock A, De Clercq D, Willems T, et al: Temporal characteristics of foot roll-over during barefoot jogging: reference data for young adults. Gait Posture 21: 432, 2005.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

A Novel Method of Quantifying Gait Deviations Using Plantar Pressure Patterns

Kevin Deschamps Department of Rehabilitation Sciences, Musculoskeletal Rehabilitation Research Group, KU Leuven, Leuven, Belgium.
Laboratory for Clinical Motion Analysis, University Hospital Pellenberg, KU Leuven, Leuven, Belgium.

Search for other papers by Kevin Deschamps in
Current site
Google Scholar
PubMed
Close
 PhD
,
Filip Staes Department of Rehabilitation Sciences, Musculoskeletal Rehabilitation Research Group, KU Leuven, Leuven, Belgium.

Search for other papers by Filip Staes in
Current site
Google Scholar
PubMed
Close
 PhD
,
Dirk Desmet Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.

Search for other papers by Dirk Desmet in
Current site
Google Scholar
PubMed
Close
 MSc
,
Philip Roosen Department of Rehabilitation Sciences and Physiotherapy, Musculoskeletal Rehabilitation Research Group, Ghent University, Ghent, Belgium.

Search for other papers by Philip Roosen in
Current site
Google Scholar
PubMed
Close
 PhD
,
Giovanni Arnoldo Matricali


Search for other papers by Giovanni Arnoldo Matricali in
Current site
Google Scholar
PubMed
Close
 MD, PhD
,
Noel Keijsers Research Department, Sint Maartenskliniek Nijmegen, Nijmegen, the Netherlands.

Search for other papers by Noel Keijsers in
Current site
Google Scholar
PubMed
Close
 PhD
,
Frank Nobels Department of Internal Medicine-Endocrinology, Multidisciplinary Diabetic Foot Clinic, Onze-Lieve-Vrouw Ziekenhuis Aalst, Aalst, Belgium.

Search for other papers by Frank Nobels in
Current site
Google Scholar
PubMed
Close
 MD, PhD
,
Jos Tits Department of Internal Medicine-Endocrinology, Multidisciplinary Diabetic Foot Clinic, Ziekenhuis Oost-Limburg, Genk, Belgium.

Search for other papers by Jos Tits in
Current site
Google Scholar
PubMed
Close
 MD
, and
Herman Bruyninckx Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.

Search for other papers by Herman Bruyninckx in
Current site
Google Scholar
PubMed
Close
 PhD
Restricted access

Background: Comparing the dynamic pedobarographic patterns of individuals is common practice in basic and applied research. However, this process is often time-consuming and complex, and commercially available software often lacks powerful visualization and interpretation tools.

Methods: We propose a simple method for displaying pixel-level pedobarographic deviations over time relative to a so-called reference pedobarographic pattern. This novel method contains four distinct automated preprocessing stages: 1) normalization of pedobarographic fields (for foot length and width), 2) temporal normalization, 3) a pixel-level z-score–based calculation, and 4) color coding of the normalized pedobarographic fields. Group and patient-level comparisons were illustrated using an experimental data set including diabetic and nondiabetic patients.

Results: The automated procedure was found to be robust and quantified distinct temporal deviations in pedobarographic fields.

Conclusions: The advantages of the novel method cover several domains, including visualization, interpretation, and education.

Department of Development and Regeneration, KU Leuven, Leuven, Belgium.

Multidisciplinary Diabetic Foot Clinic, University Hospitals Leuven, KU Leuven, Leuven, Belgium.

Corresponding author: Kevin Deschamps, PhD, Department of Rehabilitation Sciences, Musculoskeletal Rehabilitation Research Group, KU Leuven, Tervuursevest 101, B-3001 Leuven (Heverlee), Belgium. (E-mail: kevin.deschamps@faber.kuleuven.ac.be)