Curvature generation based on weight-updated boosting using shoe last point-cloud measurements

Wang, D., Li, Z., Dey, N., Misra, B., Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 and Shi, F. (2024) Curvature generation based on weight-updated boosting using shoe last point-cloud measurements. Heliyon, 10 (4). e26498. ISSN 2405-8440 doi: 10.1016/j.heliyon.2024.e26498

Abstract/Summary

Lasts are foot-shaped forms made of plastic, wood, aluminum, or 3D-printed plastic. The last of a shoe determines not only its shape and style but also how well it fits and protects the foot. A weight-updated boosting-based ensemble learning (WUBEL) algorithm is presented in this paper to extract critical features (points) from plantar pressure imaging to optimize the shoe's last surface to satisfy a comfortable shoe's last surface optimization design. An enhanced last design is constructed from the foot measurement data of the bottom surface of the base last, the critical control lines (points) of the shoe's last body, and the running-in degree of the pressure-sensitive area lattice data. Using a Likert scale (LS) and relevant evaluation indicators, we conducted an experimental evaluation and comparative study of our enhanced last design. With a point-cloud dataset, the proposed method performs highly effectively in constructing shoes, which will help diabetes patients find comfortable and customized shoes.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/115299
Identification Number/DOI	10.1016/j.heliyon.2024.e26498
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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