A Data-driven Methodology to Characterize the Dynamic Pattern of Human Motion Based on Plantar Pressure Measurements

Cabral, Henrique; Tourwé, Tom; Tsiporkova, Elena; Aerts, Wouter

doi:10.1109/is48319.2020.9200183

Cited by 2 publications

(1 citation statement)

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“…When the pressure measuring insole moves, the PP measurement system often has a large error due to signal attenuation [18][19][20][21][22]. Through thorough consideration of multiple attributes of flexible sensing materials, Neri et al [23] designed the framework for a system based on multiplexing circuit with a resistive voltage amplifier and Ardunio module, and chose Windows Forms (WinForms) and MongoDB to realize software visualization and data storage, respectively.…”

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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