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

Wang, Yu and Hao, Dongmei and Shi, Jingbin and Yang, Zeqiang and Jin, Liu and Zhang, Song and Yang, Yimin and Bin, Guangyu and Zeng, Yanjun and Zheng, Dingchang (2016) Regression models for near-infrared measurement of subcutaneous adipose tissue thickness. Physiological Measurement, 37 (7). p. 1024. ISSN 1361-6579

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Official URL: http://dx.doi.org/10.1088/0967-3334/37/7/1024

Abstract

Obesity is often associated with the risks of diabetes and cardiovascular disease, and there is a need to measure 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 an SATmeter developed in our laboratory. Near-infrared signals backscattered from the body surfaces from 40 subjects at 20 body sites each were recorded. 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 results from a mechanical skinfold caliper (MSC) and a body composition balance monitor (BCBM). The results showed that both LR- and SVR-based measurement produced better accuracy than MSC and BCBM. It was also concluded that by using regression models specifically designed for certain parts of human body, higher measurement accuracy could be achieved than using a general model for the whole body. Our results demonstrated that the SATmeter is a feasible method, which can be applied at home and in the community due to its portability and convenience.

Item Type: Journal Article
Keywords: subcutaneous adipose tissue thickness, near-infrared, PPG
Faculty: ARCHIVED Faculty of Medical Science (until September 2018)
Depositing User: Professor D Zheng
Date Deposited: 22 Jul 2016 08:02
Last Modified: 14 Nov 2019 16:12
URI: http://arro.anglia.ac.uk/id/eprint/700168

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