Smart digital podoscope for foot deformity assessment

Laowattanatham, Navaporn; Chitsakul, Kittipol; Tretriluxana, Suradej; Hansasuta, C

doi:10.1109/bmeicon.2014.7017410

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…The absolute errors of the these systems are shown in Fig. 6a, as well as the errors of the model proposed in (3) and the model proposed by Wu et al [14] in (2). We emphasize that we obtained all errors as a function of the ideal output voltage established in (5).…”

Section: A 4 × 4 Arraymentioning

confidence: 81%

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Crosstalk Error Analysis in IIDFC Readout Circuit for Use in Piezoresistive Composite

et al. 2018

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Resistive sensor arrays (RSAs) have been developed for a variety of applications depending on resolution requirements, active sensor area, and cost. Composite-based piezoresistive sensors are commonly found in RSAs. However, they present an intrinsic problem related to the parasitic current paths that flow through the whole composite, which cause a crosstalk effect. Various circuits have been proposed to reduce the crosstalk effect in sensor arrays. In this paper, we compare a number of readout circuits with PSpice models, mainly those including voltage feedback (VF) circuits and zero potential (ZP) circuits. To develop an array with 3600 sensors using a piezoresistive composite, we selected the improved isolated drive feedback circuit (IIDFC). Using nodal analysis, we were able to develop an alternative mathematical circuit model, which allowed us to identify its unique characteristics, including the influence of array size on the crosstalk effect and the restriction on minimal impedance that the array elements must attain to maintain a minimum error. In a resistive array with 3600 sensors and a sensor range of 10-100 k , we obtained an absolute error of less than 3.56%. In addition, the proposed mathematical circuit model obtained a maximum absolute error difference of 0.03% when compared with the PSpice model with the precision operational amplifier OPA37. Thus, using the IIDFC circuit and considering the minimal allowable impedance, it is possible to maintain a reduced error in an RSA with a large number of sensors.

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Section: A 4 × 4 Arraymentioning

confidence: 81%

“…The model proposed by the authors in [14] via (2) does not consider the non-scanned elements on the column selected to read the EBT, and it also disregards all non-scanned elements in the non-selected rows and columns. Thus, by nodal analysis shown in Fig.…”

Section: Mathematical Analysis Circuit D (Iidfc)mentioning

confidence: 99%

Crosstalk Error Analysis in IIDFC Readout Circuit for Use in Piezoresistive Composite

et al. 2018

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“…Diversas técnicas son útiles a este fin, desde la observación subjetiva de un especialista, hasta métodos basados en adquisición de señales. Una revisión realizada por medio de la base de datos Scopus a las publicaciones de los últimos años relacionadas con técnicas para el estudio del pie (figura 1), revela que las investigaciones en este tópico se hacen, principalmente, a través de la adquisición de las señales de presión ocasionadas por la pisada, como en Navarro et al 2010, y del análisis de la huella del pie, como en Su et al (2016) y Laowattanatham et al (2014). El uso de imágenes termográficas, en relación con estas otras técnicas, es menor; sin embargo, el uso de este tipo de imágenes ha adquirido relevancia en los últimos años, por su utilidad en el diagnóstico de diversas enfermedades.…”

Section: Materiales Y Métodosunclassified

Diseño de un sistema de reconocimiento de patrones en imágenes termográficas y de huella plantar para la identificación de pie plano en niños con edades entre cinco y seis años

et al. 2019

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The following paper presents the main results of exploratory research-oriented to the design and implementation of a pattern recognition system for flatfoot identification in children between 5 and 6 years. Patterns were determined from texture analysis of foot thermographic images, and from contour analysis of footprint images. For each case, an artificial neuronal network was trained, with base in a back-propagation algorithm. In each trial, 70 % of data were used for training, and 30 % for validation. For experiments done, success rates greater than 80 % were achieved. The best results were reached with contour patterns reduced by principal components analysis, PCA, in a binary system, with a success rate of 90.84 % in cross-validation. Results are a contribution to the study of diagnostic techniques for flatfoot treatment through the use of technologic tools.

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“…For these reasons, establishing adequate footprint detection criteria and monitoring will enable children to correctly develop a functional locomotor system [ 7 , 8 ]. In this sense, the main pathological symptoms are, on the one hand, a pronated foot (i.e., flat foot), which is the absence of a foot sole vault, and, on the other hand, a supinated foot (i.e., arched foot), which corresponds to an increaseof the foot sole arch and presents an atypical elevation that causes the stiffening of the toes [ 9 ]. However, a footprint with an adequate weight distribution and balance is considered neutral, which does not represent any harm to the patient.…”

Section: Introductionmentioning

confidence: 99%

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

Rosero-Montalvo

Fuentes-Hernández

Morocho-Cayamcela

et al. 2021

Sensors

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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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Smart digital podoscope for foot deformity assessment

Cited by 10 publications

References 6 publications

Crosstalk Error Analysis in IIDFC Readout Circuit for Use in Piezoresistive Composite

Crosstalk Error Analysis in IIDFC Readout Circuit for Use in Piezoresistive Composite

Diseño de un sistema de reconocimiento de patrones en imágenes termográficas y de huella plantar para la identificación de pie plano en niños con edades entre cinco y seis años

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

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