Regression models for near-infrared measurement of subcutaneous adipose tissue thickness

Abstract

Abstract:Obesity is often associated with the risks of diabetes and cardiovascular disease, and there is a need to measure the subcutaneous adipose tissue (SAT) thickness for acquiring the distribution of body fat. The present study aimed to develop and evaluate different model-based methods for SAT thickness measurement using a SATmeter developed in our lab. Near infrared signals backscattered from body surface were recorded from 40 subjects at 20 body sites each. Linear regression (LR) and support vector regression (SVR) models were established to predict SAT thickness on different body sites. The measurement accuracy was evaluated by ultrasound, and compared with mechanical skinfold caliper (MSC) and body composition balance monitor (BCBM). The results showed that both LR and SVR based measurement produced better accuracy than MSC and BCBM. It has also been concluded that using the regression models specifically designed for certain local parts of human body, higher measurement accuracy could be achieved than using the general model for the whole body. Our results demonstrated that SATmeter is a feasible method, which can be applied at home and community for its portability and convenience.