抄録
To 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.
| 本文言語 | 英語 |
|---|---|
| ページ（範囲） | 394-400 |
| ページ数 | 7 |
| ジャーナル | IEEJ Transactions on Electrical and Electronic Engineering |
| 巻 | 18 |
| 号 | 3 |
| DOI | |
| 出版ステータス | 出版済み - 3月 2023 |