Artificial Intelligence-Based Assessment System for Evaluating Suitable Range of Heel Height

Abstract

High-heeled shoes of excessive height can severely injure shoe users. Such shoes may cause various injuries, including musculoskeletal pain, osteoarthritis, and hallux valgus. A physician can estimate an appropriate heel height limitation for an individual wearer by touching calcaneus to estimate deformation of the calcaneal varus. It would typically be impractical for a woman to seek the professional assistance of her physician when buying high-heeled shoes. A novel system was developed in this study for evaluating the maximum safe height of high-heeled shoes for female wearers. In this study, images of Achilles tendons, medial longitudinal arches, lateral longitudinal arches, and plantar pressure distributions served as the inputs in the proposed system. After the system had been trained with those images, the system could output the maximum height of high-heeled shoes for each individual wearer. In this study, two crucial methods were used for performing the evaluating system. First, Basic CNN, VGG16, and MobileNetV2 were used to evaluate images of feet. Through the experiments, the proposed artificial intelligence (AI) model achieved an accuracy of 0.88. Next, a statistics algorithm was used to modify the results obtained from the AI model. Subsequently, the error of the system declined. The mean absolute error of the proposed system which was used for evaluating the maximum height of high-heeled shoes was 1.21 cm, which is less than the typical increment for commercially available high-heeled shoes.

Keywords

• High-heeled shoes
• calcaneal varus
• plantar pressure
• convolutional neural network (CNN)
• artificial intelligence (AI)