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. Attached Files Name Description MIMEType Size Downloads Title Automated spatial pattern analysis for identification of foot arch height from 2D foot prints Author(s) Lucas, Julien Khalaf, Kinda Charles, James orcid.org/0000-0002-9831-4205 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 complexity foot arch height wavelet analysis science & technology life sciences & biomedicine physiology 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30118409 Document type: Journal Article Collections: Faculty of Arts and Education Institute of Koorie Education Open Access Checking Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 18 Feb 2019, 11:50:26 EST Mon, 18 Feb 2019, 13:37:58 EST Mon, 18 Feb 2019, 13:54:08 EST Wed, 27 Feb 2019, 14:34:06 EST Wed, 27 Feb 2019, 14:35:43 EST Wed, 27 Feb 2019, 14:36:22 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 22 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 18 Feb 2019, 11:50:26 EST

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