Foot type classification using sensor-enabled footwear and 1D-CNN

Abstract

Poor selection of footwear, underestimation of foot health, sedentary life, and lack of accessible foot screening can have significant long-term adverse effects on the health of lower limbs. Unobtrusive, pervasive methods for automated foot screening have the potential to allow for timely detection of foot abnormalities. In the present study, we describe a proof-of-concept where data collected through sensor-enabled insoles and processed through one-dimensional convolutional neural networks were used to distinguish normal, cavus, and planus feet. We explored several combinations of sensor modalities to find the one that reflects foot types optimally. The highest accuracy of classification of 99.26% was achieved when angular velocity and force sensing were combined. Based on results, we suggest that sensor insoles, combined with optimal classification techniques, could be used for foot screening.