2016 Regression modes for near-infrared measurement.pdf (528.45 kB)
Regression models for near-infrared measurement of subcutaneous adipose tissue thickness
journal contribution
posted on 2023-08-30, 14:20 authored by Yu Wang, Dongmei Hao, Jingbin Shi, Zeqiang Yang, Liu Jin, Song Zhang, Yimin Yang, Guangyu Bin, Yanjun Zeng, Dingchang ZhengObesity 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.
History
Refereed
- Yes
Volume
37Issue number
7Page range
1024Publication title
Physiological MeasurementISSN
1361-6579External DOI
Publisher
IOP PublishingFile version
- Accepted version
Language
- eng
Official URL
Legacy posted date
2016-07-22Legacy creation date
2016-07-04Legacy Faculty/School/Department
ARCHIVED Faculty of Medical Science (until September 2018)Usage metrics
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