The non-invasive nature of pedobarographic measurements is particularly attractive to researchers for analyzing and characterizing the impact of specific pathological foot conditions. However, adequate clinical use of pedobarographic technology requires a profound technical and methodological knowledge. Several papers summarized the technical capacities of pedobarographic technology. Moreover, methodological expertise has grown considerably during the last two decades. Therefore, two crucial decisions have to be made before pathomechanical modelling or functional interpretation of foot and lower limb disorders can be pursued. The first is the selection of the specific method to analyse the dynamic plantar footprint, and the second is the choice of parameters to quantify the results. In the first part of this paper, we review the different methods used to analyse the dynamic plantar footprint and discuss their conceptual backgrounds. We also aim to illustrate the clinical relevance of each method and elaborate on the future perspectives. In the second part, we review quantification methods of pedobarographic measurements. The latter is of primary relevance to clinicians and investigators with a special interest in foot and lower limb biomechanics.

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Article Synopsis
  • * A study with 56 participants (28 healthy, 28 with KOA) used machine learning models to analyze data from pedobarography (foot pressure) and movement patterns, achieving an impressive accuracy of 89.3% in identifying KOA.
  • * Additionally, surface electromyography (sEMG) data helped differentiate between healthy individuals and KOA subjects through clustering analysis, indicating the effectiveness of using indirect measures for KOA detection.
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