Artificial neural networks in the selection of shoe lasts for people with mild diabetes.

This research addressed the selection of shoe lasts for footwear design to help relieve the pain associated with diabetic neuropathy and foot ulcers. A reverse engineering (RE) technique was used to convert point clouds corresponding to scanned shoe lasts and diabetic foot data into stereo lithograph (STL) meshes. A slicing algorithm was developed and was used to find relevant girth features of diabetic foot and the shoe lasts. An artificial neural network, termed self-organizing map (SOM), classified 60 sets of shoe lasts into similar groups. Foot shapes of three mild diabetic patients were entered into the SOM feature categories to match with suitable shoe lasts. By conducting expert questionnaire analysis of the characteristic girths featured data with analytic hierarchy process (AHP), the weights of the girths were obtained. Grey relational analysis (GRA) was then used to calculate the correlation between foot girth and the corresponding range of shoe lasts. The most suitable shoe last for each patient with a mild diabetic foot can be determined by calculating the relative fitness function for each patient. By correlating diabetic foot with suitable shoe lasts, this study demonstrated an effective strategy for designing shoes for patients with mild diabetes, which can then be manufactured to meet customized requirements.

[1]  Francisco Javier Amorós-González,et al.  A new surface joining technique for the design of shoe lasts , 2013 .

[2]  C. G. Fontanella,et al.  Analysis of heel pad tissues mechanics at the heel strike in bare and shod conditions. , 2013, Medical engineering & physics.

[3]  Michele Germani,et al.  CAD tools for designing shoe lasts for people with diabetes , 2013, Comput. Aided Des..

[4]  Ravindra S. Goonetilleke,et al.  Foot measurements from three-dimensional scans: A comparison and evaluation of different methods , 2006 .

[5]  Wei Lei,et al.  Finite element modeling of a 3D coupled foot-boot model. , 2011, Medical engineering & physics.

[6]  Michele Germani,et al.  A collaborative web-based platform for the prescription of Custom-Made Insoles , 2017, Adv. Eng. Informatics.

[7]  Arturo N. Natali,et al.  Biomechanical behavior of plantar fat pad in healthy and degenerative foot conditions , 2015, Medical & Biological Engineering & Computing.

[8]  Xiao Ma,et al.  3D foot prediction method for low cost scanning , 2014 .

[9]  S. Bus,et al.  Twelve steps per foot are recommended for valid and reliable in-shoe plantar pressure data in neuropathic diabetic patients wearing custom made footwear. , 2011, Clinical biomechanics.

[10]  Mariano Alcañiz Raya,et al.  Contact model, fit process and, foot animation for the virtual simulator of the footwear comfort , 2010, Comput. Aided Des..

[11]  Chee Kai Chua,et al.  Rapid Prototyping:Principles and Applications , 2010 .

[12]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[13]  D. Armstrong,et al.  Preventing foot ulcers in patients with diabetes. , 2005, JAMA.

[14]  Antonio Jimeno-Morenilla,et al.  Footwear bio-modelling: An industrial approach , 2013, Comput. Aided Des..

[15]  A. Boulton,et al.  Diabetic foot ulcers: A framework for prevention and care , 1999, Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society.

[16]  Nachiappan Chockalingam,et al.  The effectiveness of footwear as an intervention to prevent or to reduce biomechanical risk factors associated with diabetic foot ulceration: a systematic review. , 2013, Journal of diabetes and its complications.

[17]  Chung-Shing Wang,et al.  Feature recognition and shape design in sneakers , 2016, Comput. Ind. Eng..

[18]  J. Louwerens,et al.  The influence of shoe design on plantar pressures in neuropathic feet. , 2003, Diabetes care.

[19]  Madjid Tavana,et al.  An integrated green supplier selection approach with analytic network process and improved Grey relational analysis , 2015 .

[20]  Juan Carlos González,et al.  Virtual Shoe Test Bed: A Computer-Aided Engineering Tool for Supporting Shoe Design , 2007 .

[21]  Taho Yang,et al.  The use of a grey-based Taguchi method for optimizing multi-response simulation problems , 2008 .

[22]  Ameersing Luximon,et al.  Foot landmarking for footwear customization , 2003, Ergonomics.

[23]  Channa P. Witana,et al.  Dimensional differences for evaluating the quality of footwear fit , 2004, Ergonomics.

[24]  Xinguo Ming,et al.  Prioritising technical attributes in QFD under vague environment: a rough-grey relational analysis approach , 2014 .

[25]  Tae Jin Hwang,et al.  Derivation of template shoe-lasts for efficient fabrication of custom-ordered shoe-lasts , 2005, Comput. Aided Des..

[26]  Kin-Chuen Hui,et al.  Human foot modeling towards footwear design , 2011, Comput. Aided Des..

[27]  Yan Luximon,et al.  Shoe-last design innovation for better shoe fitting , 2009, Comput. Ind..

[28]  C. Nester,et al.  Effect of rocker shoe design features on forefoot plantar pressures in people with and without diabetes. , 2013, Clinical biomechanics.

[29]  Teuvo Kohonen,et al.  Essentials of the self-organizing map , 2013, Neural Networks.

[30]  Yueqi Zhong,et al.  Automatic foot scanning and measurement based on multiple RGB-depth cameras , 2018 .

[31]  Hui-Mei Yang,et al.  Risk factors for lower extremity amputation in diabetic foot disease categorized by Wagner classification. , 2012, Diabetes research and clinical practice.

[32]  H. King,et al.  Global Burden of Diabetes, 1995–2025: Prevalence, numerical estimates, and projections , 1998, Diabetes Care.

[33]  Ahmet Erdemir,et al.  Reduction of plantar heel pressures: Insole design using finite element analysis. , 2006, Journal of biomechanics.

[34]  Der-Baau Perng,et al.  A systematic approach for developing a foot size information system for shoe last design , 2000 .

[35]  Chung-Shing Wang.  An analysis and evaluation of fitness for shoe lasts and human feet , 2010, Comput. Ind..

[36]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .

[37]  Mariano Alcañiz Raya,et al.  Artificial neural networks for predicting dorsal pressures on the foot surface while walking , 2012, Expert Syst. Appl..

[38]  Meng-Dar Shieh,et al.  Developing a design support system for the exterior form of running shoes using partial least squares and neural networks , 2013, Comput. Ind. Eng..

[39]  Kevin Woo,et al.  Diabetic foot ulcers: Part I. Pathophysiology and prevention. , 2014, Journal of the American Academy of Dermatology.

[40]  Zuhua Jiang,et al.  A computer-aided design system for foot-feature-based shoe last customization , 2010 .

[41]  Youlian Hong,et al.  Gender differences in foot shape: a study of Chinese young adults , 2011, Sports biomechanics.

[42]  S. H. Choi,et al.  Hierarchical slice contours for layered-manufacturing , 2002, Comput. Ind..

[43]  Wen-Ming Chen,et al.  Plantar pressure relief under the metatarsal heads: therapeutic insole design using three-dimensional finite element model of the foot. , 2015, Journal of biomechanics.