A Knowledge-Based Modeling for Plantar Pressure Image Reconstruction

It is known that prolonged pressure on the plantar area is one of the main factors in developing foot ulcers. With current technology, electronic pressure monitoring systems can be placed as an insole into regular shoes to continuously monitor the plantar area and provide evidence on ulcer formation process as well as insight for proper orthotic footwear design. The reliability of these systems heavily depends on the spatial resolution of their sensor platforms. However, due to the cost and energy constraints, practical wireless in-shoe pressure monitoring systems have a limited number of sensors, i.e., typically
$K<10$
. In this paper, we present a knowledge-based regression model (SCPM) to reconstruct a spatially continuous plantar pressure image from a small number of pressure sensors. This model makes use of high-resolution pressure data collected clinically to train a per-subject regression function. SCPM is shown to outperform all other tested interpolation methods for
$K<60$
sensors, with less than one-third of the error for
$K=10$
sensors. SCPM bridges the gap between the technological capability and medical need and can play an important role in the adoption of sensing insole for a wide range of medical applications.