Design element extraction of plantar pressure imaging employing meta-learning-based graphic convolutional neural networks

Abstract

Segmenting plantar pressure images intelligently can provide valuable insight for people with high blood pressure, making bespoke footwear requirements possible and resulting in more comfortable shoe designs. It is, however, difficult to extract design elements from a segmented image dataset. To address this challenge, we propose an ML-GNN model that segments plantar pressure images using metal-earning. The first part of the paper presents a method for extracting image features that reduce the complexity of the ML-GNN algorithm. To create the network structure, we propose optimization meta-based learning. Using a meta-learning-based graphic neural network, we enhance our mask-based CNN prediction model with VGG16 and CNN layers. We pre-processed the plantar pressure dataset using pressure-sensing data acquisition and compared the results. By defining standard image segmentation indices, we demonstrate the high effectiveness of our research. We have developed an ML-GNN model that improves the segmentation accuracy of plantar pressure images and can also be applied to other sensor image datasets. Through our shoe-last customization approach, we enable the shoe industry to manufacture shoes more efficiently, particularly for people with specific healthcare needs who require bespoke shoe designs. Our findings demonstrate the potential of intelligent image segmentation to advance the field of footwear design and improve the lives of people with specific health requirements.