Automated spatial pattern analysis for identification of foot arch height from 2D foot prints

Lucas, Julien, Khalaf, Kinda, Charles, James, Leandro, Jorge JG and Jelinek, Herbert F 2018, Automated spatial pattern analysis for identification of foot arch height from 2D foot prints, Frontiers in physiology, vol. 9, pp. 1-8, doi: 10.3389/fphys.2018.01216.

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Title Automated spatial pattern analysis for identification of foot arch height from 2D foot prints
Author(s) Lucas, Julien
Khalaf, Kinda
Charles, JamesORCID iD for Charles, James
Leandro, Jorge JG
Jelinek, Herbert F
Journal name Frontiers in physiology
Volume number 9
Article ID 1216
Start page 1
End page 8
Total pages 8
Publisher Frontiers Media
Place of publication Lausanne, Switzerland
Publication date 2018-09
ISSN 1664-042X
Keyword(s) non-linear dynamics
bending energy
foot arch height
wavelet analysis
science & technology
life sciences & biomedicine
Summary Arch height is an important determinant for the risk of foot pathology, especially in an aging population. Current methods for analyzing footprints require substantial manual processing time. The current research investigated automated determination of foot type based on features derived from the Gabor wavelet utilizing digitized footprints to allow timely assessment of foot type and focused intervention. Two hundred and eighty footprints were collected, and area, perimeter, curvature, circularity, 2nd wavelet moment, mean bending energy (MBE), and entropy were determined using in house developed MATLAB codes. The results were compared to the gold standard using Spearman's Correlation coefficient and multiple linear regression models with significance set at 0.05. The proposed approach found MBE combined with foot perimeter to give the best results as shown by ANOVA (F(2,211) = 10.18, p < 0.0001) with the mean ±SD of low, normal, and high arch being, respectively, 0.26 ± 0.025,.24 ± 0.021, and 0.23 ± 0.024. A clinical review of the new cut off values, as set by the first and the third quartiles of our sample, lead to reliability up to 87%. Our results suggest that automated wavelet-based foot type classification of 2D binary images of the plantar surface of the foot is comparable to current state-of-the-art methods providing a cost and time effective tool suitable for clinical diagnostics.
Language eng
DOI 10.3389/fphys.2018.01216
Field of Research 110699 Human Movement and Sports Science not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, Lucas, Khalaf, Charles, Leandro and Jelinek
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