Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

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Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: Paul D. Rosero-Montalvo, Edison A. Fuentes-Hernández, Manuel E. Morocho-Cayamcela, Luz M. Sierra-Martínez, Diego H. Peluffo-Ordóñez

Publisher: MDPI

Publication year: 2021

Journal: Sensors (1424-8220)

ISSN: 1424-8220

eISSN: 1424-8220


Languages: English (EN-GB)

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The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.


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Last updated on 2021-16-09 at 23:16