Enhancing plantar pressure distribution reconstruction with conditional generative adversarial networks from multi-region foot pressure sensing

Abstract

Estimating foot pressure distribution and the center of pressure (COP) using a sparse sensor topology offers cost-effective benefits. While deep learning neural networks improve the prediction of information in areas with incomplete sensing, there are still gaps in foot pressure recordings due to limited sensor coverage in certain plantar regions. To address this, we used eleven larger sensors to increase coverage across critical foot areas, including the big toe, little toe, medial, middle, and lateral metatarsus, as well as the medial and lateral arches, foreheels, and heels. These regions are commonly used to study the effects of muscle fatigue during walking and jogging, as well as to predict ground reaction forces during walking. We employed a conditional generative adversarial network (GAN) to reconstruct high-resolution foot pressure distributions from the data collected by these sensors. This method operates on individual samples, eliminating the need for gait cycle segmentation and normalization. Compared to ground truth data from a 99-sensor array, the GAN approach significantly improved COP estimation over direct computation from the eleven sensors. The highest accuracy was achieved during level walking, with reduced performance during jogging and stair walking. In conclusion, the conditional GAN effectively reconstructed foot pressure distributions, and future research should explore reallocating sensor topology to improve resolution and coverage while balancing simplified instrumentation with improved plantar pressure distribution reconstruction.