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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 describeshuman walking features. It is proved that once one has trouble with lower limb,the distribution of plantar pressure will change to some degree. Plantarpressure can be converted into images according to some simple standards. Inthis paper, we take full advantage of these plantar pressure images for medicalusage. We present N2RPP, a generative adversarial network (GAN) based method torebuild 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 usefulrepresentation to describe and rebuild plantar pressure images. According toN2RPP's results, we find out that there are several noteworthy differencesbetween normal people and patients. This can provide doctors a rough directionof adjusting plantar pressure to a better distribution to reduce patients' soreand pain during the rehabilitation treatment for ACLD.

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