Detecting pediatric foot deformities using plantar pressure measurements: A semi-supervised approach

K Ramakrishna Kini; Muddu Madakyaru; Fouzi Harrou; Ying Sun

doi:10.1109/mdat.2023.3234224

Detecting pediatric foot deformities using plantar pressure measurements: A semi-supervised approach

K Ramakrishna Kini, Muddu Madakyaru, Fouzi Harrou, Ying Sun

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

This paper presents a semi-supervised data-driven approach to identify pediatric foot deformities using foot plantar pressure measurements. Essentially, the developed approach merges the desirable features of the kernel principal components analysis as a feature extractor and the Kantorovich Distance-driven monitoring scheme for detecting pediatric foot deformities. For extending the flexibility of the proposed scheme, kernel density estimation based nonparametric decision threshold is adopted. The method is assessed via publically available data containing three types of footsteps (i.e., normal, flat, and cavus). The detection results show that the method proved promising results, thus, outperforming commonly applied monitoring schemes.

Original language	English (US)
Pages (from-to)	1-1
Number of pages	1
Journal	IEEE Design & Test
DOIs	https://doi.org/10.1109/mdat.2023.3234224
State	Published - Jan 4 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-01-09
Acknowledged KAUST grant number(s): OSR-2019- CRG7-3800
Acknowledgements: This work was supported by the King Abdullah University of Science and Technology, Office of Sponsored Research (OSR) under Award OSR-2019- CRG7-3800

ASJC Scopus subject areas

Software
Electrical and Electronic Engineering
Hardware and Architecture

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10.1109/mdat.2023.3234224

https://repository.kaust.edu.sa/bitstream/10754/686854/1/Detecting_pediatric_foot_deformities_using_plantar_pressure_measurements_A_semi-supervised_approach.pdf

Cite this

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title = "Detecting pediatric foot deformities using plantar pressure measurements: A semi-supervised approach",

abstract = "This paper presents a semi-supervised data-driven approach to identify pediatric foot deformities using foot plantar pressure measurements. Essentially, the developed approach merges the desirable features of the kernel principal components analysis as a feature extractor and the Kantorovich Distance-driven monitoring scheme for detecting pediatric foot deformities. For extending the flexibility of the proposed scheme, kernel density estimation based nonparametric decision threshold is adopted. The method is assessed via publically available data containing three types of footsteps (i.e., normal, flat, and cavus). The detection results show that the method proved promising results, thus, outperforming commonly applied monitoring schemes.",

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AU - Kini, K Ramakrishna

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AU - Harrou, Fouzi

AU - Sun, Ying

N1 - KAUST Repository Item: Exported on 2023-01-09 Acknowledged KAUST grant number(s): OSR-2019- CRG7-3800 Acknowledgements: This work was supported by the King Abdullah University of Science and Technology, Office of Sponsored Research (OSR) under Award OSR-2019- CRG7-3800

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N2 - This paper presents a semi-supervised data-driven approach to identify pediatric foot deformities using foot plantar pressure measurements. Essentially, the developed approach merges the desirable features of the kernel principal components analysis as a feature extractor and the Kantorovich Distance-driven monitoring scheme for detecting pediatric foot deformities. For extending the flexibility of the proposed scheme, kernel density estimation based nonparametric decision threshold is adopted. The method is assessed via publically available data containing three types of footsteps (i.e., normal, flat, and cavus). The detection results show that the method proved promising results, thus, outperforming commonly applied monitoring schemes.

AB - This paper presents a semi-supervised data-driven approach to identify pediatric foot deformities using foot plantar pressure measurements. Essentially, the developed approach merges the desirable features of the kernel principal components analysis as a feature extractor and the Kantorovich Distance-driven monitoring scheme for detecting pediatric foot deformities. For extending the flexibility of the proposed scheme, kernel density estimation based nonparametric decision threshold is adopted. The method is assessed via publically available data containing three types of footsteps (i.e., normal, flat, and cavus). The detection results show that the method proved promising results, thus, outperforming commonly applied monitoring schemes.

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Detecting pediatric foot deformities using plantar pressure measurements: A semi-supervised approach

Abstract

Bibliographical note

ASJC Scopus subject areas

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