Continuous plantar pressure modeling using sparse sensors

Abstract

The foot complications constitute a tremendous challenge for diabetic patients, caregivers, and the healthcare system. With current technology, in-shoe monitoring systems can be implemented to continuously monitor foot's at-risk ulceration sites and send feedback to patients and physicians. The few available high resolution in-shoe pressure measuring systems are extremely expensive and targeting clinical use only. The more affordable price ranges can be reached by limiting the number of sensors in the shoe. Precise subject-specific sensor placement is still a challenge in such platforms. Moreover, there is no good way to estimate pressure on other points of the foot. In this paper, we address these technical challenges by proposing SCPM algorithm that reconstructs a continuous foot plantar pressure image from a sparse set of sensor readings. Using our technique, sensor placement can be the same in every electronic insole. However, the SCPM's trained parameters are unique for every subject and foot.