Abstract
AbstractTo predict the risk of pressure injuries in home‐care, it is important to quantify deformation of internal tissue. However, it is difficult for users in home‐care to use conventional modalities such as MRI because the modalities are expensive and not portable for the trunk. Therefore, we have developed a deformation estimation method in a sitting position with the use of machine learning and a low‐cost portable pressure mapping system. We defined the deformation as estimated soft tissue thickness values on each cell of pressure distribution. The thickness on a cell was estimated from measured pressure distribution and no‐load thickness. A no‐load thickness was prepared before estimation using a model. In this article, the estimation method was applied to two single‐layered phantoms which were constructed from wood as an ischial tuberosity and two gels as muscle and fat. The estimation errors were only 1.03% and 2.56% in the muscle and fat phantoms, respectively. We confirmed the estimation errors were adequately small values. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEJ Transactions on Electrical and Electronic Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
