Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Wang, D., Li, Z., Dey, N., Ashour, A. S., Moraru, L., Sherratt, S. ORCID: https://orcid.org/0000-0001-7899-4445 and Shi, F. (2020) Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks. Biocybernetics and Biomedical Engineering, 40 (1). pp. 546-558. ISSN 0208-5216 doi: 10.1016/j.bbe.2020.01.004

Abstract/Summary

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/88481
Identification Number/DOI	10.1016/j.bbe.2020.01.004
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	Elsevier
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