Unsupervised Generative Adversarial Network for Plantar Pressure Image-to-Image Translation

Analyzing human gait from plantar pressure is critical for human health. The majority of works focus on classifying the healthy plantar pattern from unhealthy ones. Different from previous works, we adopt a generative adversarial network to produce healthy plantar pressure image for individual patients. In this work, we do not have pairs of images for training thus we cast the problem as an unsupervised generative adversarial learning task. Our network benefits from multiple components: an encoder-decoder generator, a convolution-based discriminator, a convolution-based evaluation network, and a new term in the loss function to preserve the person's gait style. Our method achieves high performance (99.8%) on the CAD WALK databases which have patients with hallux valgus disease.