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 journalArticlepeer-review

2 Scopus citations


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 languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Design & Test
StatePublished - 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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