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Sensors 2017, 17(11), 2700; https://doi.org/10.3390/s17112700

Low Computational-Cost Footprint Deformities Diagnosis Sensor through Angles, Dimensions Analysis and Image Processing Techniques

1
Unidad Académica de Ingeniería Biomédica, Universidad Politécnica de Sinaloa, Carretera Municipal Libre Mazatlán Higueras km 3, Col. Genaro Estrada, Mazatlán Sin. 82199, Mexico
2
Center for Biomedical Technology, Polythecnic University of Madrid, Campus Montegancedo, Pozuelo de Alarcón, Madrid 28223, Spain
3
Departamento de Ingeniería Electrónica, División de Ingenierías, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8, Comunidad de Palo Blanco, Salamanca Gto. C.P. 36885, Mexico
4
CONACYT, Centro de Investigación en Matemáticas (CIMAT), A.C., Jalisco S/N, Col. Valenciana, Guanajuato Gto. 36000, Mexico
5
CA Ingeniería de Biosistemas, División de Investigación y Posgrado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las campanas S/N, Santiago de Querétaro Qro. 76010, Mexico
6
Facultad de Ciencias Físico-Matemáticas, Universidad Autónoma de Sinaloa, Av. De las Américas y Blvd. Universitarios, Cd. Universitaria, Culiacán Sin. 80000, Mexico
*
Author to whom correspondence should be addressed.
Received: 19 October 2017 / Revised: 9 November 2017 / Accepted: 14 November 2017 / Published: 22 November 2017
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Abstract

Manual measurements of foot anthropometry can lead to errors since this task involves the experience of the specialist who performs them, resulting in different subjective measures from the same footprint. Moreover, some of the diagnoses that are given to classify a footprint deformity are based on a qualitative interpretation by the physician; there is no quantitative interpretation of the footprint. The importance of providing a correct and accurate diagnosis lies in the need to ensure that an appropriate treatment is provided for the improvement of the patient without risking his or her health. Therefore, this article presents a smart sensor that integrates the capture of the footprint, a low computational-cost analysis of the image and the interpretation of the results through a quantitative evaluation. The smart sensor implemented required the use of a camera (Logitech C920) connected to a Raspberry Pi 3, where a graphical interface was made for the capture and processing of the image, and it was adapted to a podoscope conventionally used by specialists such as orthopedist, physiotherapists and podiatrists. The footprint diagnosis smart sensor (FPDSS) has proven to be robust to different types of deformity, precise, sensitive and correlated in 0.99 with the measurements from the digitalized image of the ink mat. View Full-Text
Keywords: embedded system; footprint measurements; Staheli arch index; Clarke’s angle; Smirak-Chippaux index; biomedical image processing embedded system; footprint measurements; Staheli arch index; Clarke’s angle; Smirak-Chippaux index; biomedical image processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Maestre-Rendon, J.R.; Rivera-Roman, T.A.; Sierra-Hernandez, J.M.; Cruz-Aceves, I.; Contreras-Medina, L.M.; Duarte-Galvan, C.; Fernandez-Jaramillo, A.A. Low Computational-Cost Footprint Deformities Diagnosis Sensor through Angles, Dimensions Analysis and Image Processing Techniques. Sensors 2017, 17, 2700.

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