Background: Plantar pressure is widely used to evaluate foot complaints. However, most plantar pressure studies focus on the symptomatic foot with foot deformities. The purposes of this study were to investigate subjects without clear foot deformities and to identify differences in plantar pressure pattern between subjects with and without forefoot pain. The second aim was to discriminate between subjects with and without forefoot pain based on plantar pressure measurements using neural networks. Methods: In total, 297 subjects without foot deformities of whom almost 50% had forefoot pain walked barefoot over a pressure plate. Foot complaints and subject characteristics were assessed with a questionnaire and a clinical evaluation. Plantar pressure was analyzed using a recently developed method, which produced pressure images of the time integral, peak pressure, mean pressure, time of activation and deactivation, and total contact time per pixel. After pre-processing the pressure images with principal component analysis, a forward selection procedure with neural networks was used to classify forefoot pain. Findings: The pressure-time integral and mean pressure were significantly larger under the metatarsals II and III for subjects with forefoot pain. A neural network with 14 input parameters correctly classified forefoot pain in 70.4% of the test feet. Interpretation: The differences in plantar pressure parameters between subjects with and without forefoot pain were small. The reasonable performance of forefoot pain classification by neural networks suggests that forefoot pain is related more to the distribution of the pressure under the foot than to the absolute values of the pressure at fixed locations. © 2013 Elsevier Ltd. All rights reserved.
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