2019
DOI: 10.1016/j.medengphy.2018.12.014
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Artificial neural networks in the selection of shoe lasts for people with mild diabetes

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Cited by 13 publications
(10 citation statements)
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“…In the research of shoe lasts, Xiong,SP et al [8]collected 18 characteristic parameters of user's foot through CAD system, and adjusted the last shape of existing shoes through the characteristic parameters to finally achieve the purpose of customized production.Luximon,A et al [9]and Wang,J et al [10]made local modifications to the last parameters according to the biomechanical characteristics of the foot on the basis of reverse engineering, which increased the foot comfort while reducing the development cycle of the last.Huang,S et al [11]proposed a data-driven generative adversarial network(GAN)3D shoe last generation framework based on a subjective comfort evaluation approach.Mishra,MK et al [12]modeled the shoe last in a virtual environment to optimize the 3D cushioning margin between the foot and the last.Wang,CC et al [13]used reverse engineering to scan the sugar foot and last and calculated the correlation between perimeter weights and last with the help of AHP and grey correlation analysis (GRA).Table 2-1 shows some of the research results in the field of foot, last and shoe. Chen,X et al [15]used 3D free-form surface technique for inverse reconstruction of shoe last surfaces.…”
Section: Related Workmentioning
confidence: 99%
“…In the research of shoe lasts, Xiong,SP et al [8]collected 18 characteristic parameters of user's foot through CAD system, and adjusted the last shape of existing shoes through the characteristic parameters to finally achieve the purpose of customized production.Luximon,A et al [9]and Wang,J et al [10]made local modifications to the last parameters according to the biomechanical characteristics of the foot on the basis of reverse engineering, which increased the foot comfort while reducing the development cycle of the last.Huang,S et al [11]proposed a data-driven generative adversarial network(GAN)3D shoe last generation framework based on a subjective comfort evaluation approach.Mishra,MK et al [12]modeled the shoe last in a virtual environment to optimize the 3D cushioning margin between the foot and the last.Wang,CC et al [13]used reverse engineering to scan the sugar foot and last and calculated the correlation between perimeter weights and last with the help of AHP and grey correlation analysis (GRA).Table 2-1 shows some of the research results in the field of foot, last and shoe. Chen,X et al [15]used 3D free-form surface technique for inverse reconstruction of shoe last surfaces.…”
Section: Related Workmentioning
confidence: 99%
“…However, the solutions proposed in them significantly improve the comfort of use and even slightly improve the visual appearance of this type of footwear. The work of Villarreal-Calva et al [35] developed a footwear model for clubfoot. Clubfoot is a birth defect in which one or both feet are rotated inward and downward [48].…”
Section: Rq2: What Is the Annual Number Of Shoe Last Customization St...mentioning
confidence: 99%
“…This research question aims to acquire knowledge that includes an overview of data acquisition methods for creating a shoe last model. As a rule, this is anthropometric data including basic measurements of the client's feet -lengths, girths, and, in some cases, consideration of biomechanical, ergonomic, and aesthetic parameters [34][35][36][38][39][40]. Data can be acquired manually using a ruler or a Brannock instrument when capturing customer foot measurements.…”
Section: Rq4: Which Research Implements the Last Modeling Based On 3d...mentioning
confidence: 99%
“…An expert system for the fabrics selection to design transformable clothing based on user's level of quality is developed in Zakharkevich et al (2018). In the area of footwear industry, artificial neural networks (ANN) are used to develop a system for shoe lasts selection with mild diabetes (Wang et al, 2019) and for the estimation of the maximum pressure over the foot plantar surface exerted by a two-layer shoe sole for three distinct phases of the gait cycle (Xidias et al, 2015). Automated foot model reconstruction is accomplished through the use of the self-organizing Growing Neural Gas network (Jimeno-Morenilla et al, 2016).…”
Section: Artificial Intelligence For Design and Manufacturingmentioning
confidence: 99%