A Study on Thickness Estimation of Human Soft Tissue in a Sitting Position Using a Pressure Mapping System

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.