N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients

TL;DR

This paper introduces N2RPP, a GAN-based method that reconstructs plantar pressure images of ACLD patients from low-dimensional features, aiding medical diagnosis and rehabilitation by highlighting pressure distribution differences.

Contribution

The study presents a novel GAN approach combined with autoencoder features to effectively rebuild plantar pressure images for ACLD patients, enhancing medical analysis.

Findings

Extracted features effectively represent plantar pressure images.

N2RPP reveals differences between normal and ACLD patients.

Reconstructed images can guide pressure adjustment during rehabilitation.

Abstract

Foot is a vital part of human, and lots of valuable information is embedded. Plantar pressure is one of which contains this information and it describes human walking features. It is proved that once one has trouble with lower limb, the distribution of plantar pressure will change to some degree. Plantar pressure can be converted into images according to some simple standards. In this paper, we take full advantage of these plantar pressure images for medical usage. We present N2RPP, a generative adversarial network (GAN) based method to rebuild plantar pressure images of anterior cruciate ligament deficiency (ACLD) patients from low dimension features, which are extracted from an autoencoder. Through the result of experiments, the extracted features are a useful representation to describe and rebuild plantar pressure images. According to N2RPP's results, we find out that there are several noteworthy differences between normal people and patients. This can provide doctors a rough direction of adjusting plantar pressure to a better distribution to reduce patients' sore and pain during the rehabilitation treatment for ACLD.