  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.
