Plantar Pressure Measurement System With Improved Isolated Drive Feedback Circuit and ANN: Development and Characterization

Castro, Fabian; Savaris, Weslin; Araújo, Rafael Gavassa de; Costa, Andressa; Sanches, Marcelo Augusto Assunção; Carvalho, Aparecido Augusto de

doi:10.1109/jsen.2020.2998700

Cited by 4 publications

(1 citation statement)

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“…Anzai et al [24] collected PP and foot pose information with a sensor array, reduced the dimensionality and noise level of the collected data through singular value decomposition (SVD) and principal component analysis (PCA), and established a pose-based gait model to correct the error of kinematical modeling, which arises from the omission of foot movement. Castro et al [25] analyzed the PP features of flatfeet based on the PP distribution images of youngsters, and drew two important conclusions through contrastive experiments: the flatfoot group had a much higher momentum in different areas of the foot than the normal foot group, and the pressure trajectory at the pressure center skewed outside among the flatfeet subjects. Sadler et al [26] collected the PP distribution images of multiple modes of motions, namely, walking, running, ascending steps, and descending steps, divided the PP areas in the images into eight regular arrays, constructed a solving equation for the point of action for the resultant ground reaction force to the planar under the multi-motion model, and obtained the influence law of the peak force in different plantar bearing areas on the plantar force.…”

Section: Introductionmentioning

confidence: 99%

An Image Classification Algorithm for Plantar Pressure Based on Convolutional Neural Network

Zou¹,

Zhang²,

Ma³

2021

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Human structure-based plantar pressure (PP) analysis has been widely used in medical, sports, footwear design, and footwear sales. The current studies mostly focus on the development of PP measuring technologies and the analysis of pressure distribution features based on sensing results. Relatively few scholars have tried to analyze PP through image processing. To bridge the gap, this paper tries to classify PP images based on convolutional neural network (CNN). Firstly, the authors prepared the zoning and center calculation for PP images, and established a PP image classification model. Then, the PP image features were selected dynamically based on sparse, low-redundancy feature subsets, and the results of principal component analysis (PCA) were combined with the CNN to realize dynamic extraction of features from PP images. Finally, an image classification algorithm was designed based on the inter-area difference in PP distribution. The proposed algorithm was proved feasible through experiments. The research findings provide a reference for processing pressure images in other scenarios.

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Section: Introductionmentioning

confidence: 99%