N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients
This work addresses the need for better rehabilitation tools for ACLD patients by providing a method to analyze plantar pressure differences, though it is incremental as it applies existing GAN techniques to a specific medical domain.
The paper tackles the problem of rebuilding plantar pressure images for ACLD patients using a GAN-based method called N2RPP, which extracts low-dimensional features from an autoencoder and generates images that reveal differences between patients and normal people, aiding in rehabilitation adjustments.
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.