Convolutional Network for Plantar Pressure Parsing

Plantar pressure has been put in use in clinical research for decades, such as in digital human modeling, biomechanics studies, and foot surgeries. Plantar pressure indicates the stress distribution of the foot to the ground, which also could reflect the state of the arch height. Abnormal arch height would incur lower limb imbalance problem, and would further cause joint disorders if was not properly treated as soon as possible, so a measurement of the severity of abnormal arch height is important. In this paper, we present a new framework of automatic annotation that using plantar pressure for index calculation of arch height, a new approach that could share knowledge from and to clinical studies. We addressed plantar pressure parsing problem as two separate tasks, landmark detection and semantic segmentation, and proposed Plantar Parsing U-Net like (PPU-Net) fully convolutional network for the tasks. Experiment results will show that we have achieved an excellent precision with proposed single model in landmark detection while keeping the comparable performance in palm area segmentation compared to baseline models.