Detecting high-risk regions for pressure ulcer risk assessment

Pressure ulcer is a major problem for bed-bound and wheelchair-bound individuals specially in regions like sacrum, buttocks, hip, heels, back and head. Once developed, it is extremely uncomfortable and costly. Identification and monitoring of high-risk regions and their pressure distributions help nurses to have information about risk in each specific area of body and reposition patient efficiently. In this paper, we propose an algorithm to detect regions that are under high stress. Because of low resolution nature of pressure image and changes in shape of human body parts in various images, we adopted image processing algorithms. The image of human body is segmented using Delaunay triangulation. The extracted tree is compared to defined template for each posture. Then, signal processing and graph matching algorithms are used to label the tree according to the template. Pressure values of each specific region are collected for other phases of ulcer management such as risk assessment and reposition schedule. The experimental results indicate that our method can detect 9 (6) regions in supine (side) postures with average accuracy of 85.7%.