Automated Design of Affordable Modular Custom Insoles By Multi-Classification Algorithms

Abstract

Custom designed insoles are a niche product that is not always affordable to all who need them. When commercial insoles are fabricated using advanced technologies, the insoles in this study are assembled out of pre-cut modular components to keep the production cost down, hence their price. In this study, algorithms driven by a fuzzy inference were proposed in comparison with a decision tree in order to select the best component combination. One hundred and twelve subjects were recruited to collect foot data extracted from their foot images. Approximately 95% of 182 AI-designed insole pads were found in perfect agreement with the professional podiatrist’s decision with acceptable 5% deviation. Differences in the algorithms’ strength were also discussed. In addition to their superior performance, both algorithms allow the podiatrists to speed up the diagnosis and design phases. This approach, when integrated with applications of mobile devices for remotely retrieving foot data, will expand another simple yet effective customer-oriented product design service.