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Abstract: 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. [Copyright &y& Elsevier]

Abstract: Foot size and shape vary between individuals and the foot adopts arbitrary stance phase postures, so traditional pedobarographic analyses regionalize foot pressure images to afford homologous data comparison. An alternative approach that does not require explicit anatomical labelling and that is used widely in other functional imaging domains is to register images such that homologous structures optimally overlap and then to compare images directly at the pixel level. Image registration represents the preprocessing cornerstone of such pixel-level techniques, so its performance warrants independent attention. The purpose of this study was to evaluate the performance of four between-subjects warping registration algorithms including: Principal Axes (PA), four-parameter Optimal Scaling (OS4), eight-parameter Optimal Projective (OP8), and locally affine Nonlinear (NL). Fifteen subjects performed 10 trials of self-paced walking, and their peak pressure images were registered within-subjects using an optimal rigid body transformation. The resulting mean images were then registered between-subjects using all four methods in all 210 (15×14) subject combinations. All registration methods improved alignment, and each method performed qualitatively well for certain image pairs. However, only the NL consistently performed satisfactorily because of disproportionate anatomical variation in toe lengths and rearfoot/forefoot width, for example. Using three independent image (dis)similarity metrics, MANOVA confirmed that the NL method yielded superior registration performance (p <0.001). These data demonstrate that nonlinear spatial warping is necessary for robust between-subject pedobarographic image registration and, by extension, robust homologous data comparison at the pixel level. [Copyright &y& Elsevier]

Highlights: [•] Foot length and medial arch (MLA) angle during stance phase at 3 speed conditions were investigated. [•] The foot lengthens during weight bearing and shortens during propulsion. [•] At higher walking speeds there is less change in foot length and MLA angle compared to slower speeds. [•] The foot length and MLA curve were similar except between 50% and 80% of stance. [•] Foot length represents Hicks mechanism in the foot and is easy to implement in gait analysis. [Copyright &y& Elsevier]