Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities
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 emb...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/13/4422 |
id |
doaj-33b391a5aa0340f1be12cec052d251e7 |
---|---|
record_format |
Article |
spelling |
doaj-33b391a5aa0340f1be12cec052d251e72021-07-15T15:45:27ZengMDPI AGSensors1424-82202021-06-01214422442210.3390/s21134422Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot DeformitiesPaul D. Rosero-Montalvo0Edison A. Fuentes-Hernández1Manuel E. Morocho-Cayamcela2Luz M. Sierra-Martínez3Diego H. Peluffo-Ordóñez4Department of Computer Science, IT University of Copenhagen, 2300 Copenhagen, DenmarkDepartment of Technologies, Instituto Superior Tecnológico 17 de Julio, Urcuquí 100650, EcuadorIntelligence for Embedded Systems—Research Line, SDAS Research Group, Ibarra 100150, EcuadorSystems Department, Faculty of Electronic Engineering and Telecommunications, Universidad del Cauca, Popayán 190001, ColombiaIntelligence for Embedded Systems—Research Line, SDAS Research Group, Ibarra 100150, EcuadorThe 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.https://www.mdpi.com/1424-8220/21/13/4422childrenplantar pressureembedded systemsdata analysismachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Paul D. Rosero-Montalvo Edison A. Fuentes-Hernández Manuel E. Morocho-Cayamcela Luz M. Sierra-Martínez Diego H. Peluffo-Ordóñez |
spellingShingle |
Paul D. Rosero-Montalvo Edison A. Fuentes-Hernández Manuel E. Morocho-Cayamcela Luz M. Sierra-Martínez Diego H. Peluffo-Ordóñez Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities Sensors children plantar pressure embedded systems data analysis machine learning |
author_facet |
Paul D. Rosero-Montalvo Edison A. Fuentes-Hernández Manuel E. Morocho-Cayamcela Luz M. Sierra-Martínez Diego H. Peluffo-Ordóñez |
author_sort |
Paul D. Rosero-Montalvo |
title |
Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities |
title_short |
Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities |
title_full |
Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities |
title_fullStr |
Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities |
title_full_unstemmed |
Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities |
title_sort |
addressing the data acquisition paradigm in the early detection of pediatric foot deformities |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-06-01 |
description |
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. |
topic |
children plantar pressure embedded systems data analysis machine learning |
url |
https://www.mdpi.com/1424-8220/21/13/4422 |
work_keys_str_mv |
AT pauldroseromontalvo addressingthedataacquisitionparadigmintheearlydetectionofpediatricfootdeformities AT edisonafuenteshernandez addressingthedataacquisitionparadigmintheearlydetectionofpediatricfootdeformities AT manuelemorochocayamcela addressingthedataacquisitionparadigmintheearlydetectionofpediatricfootdeformities AT luzmsierramartinez addressingthedataacquisitionparadigmintheearlydetectionofpediatricfootdeformities AT diegohpeluffoordonez addressingthedataacquisitionparadigmintheearlydetectionofpediatricfootdeformities |
_version_ |
1721298499490807808 |