Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network.
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China. lmn@jlu.edu.cn.
Meina
M
Li
Department of Electronics Engineering, Chosun University, Gwangju 61452, Korea. kwak@chosun.ac.kr.
Keun-Chang
KC
Kwak
Department of IT Fusion Technology, Graduate School, Chosun University, Gwangju 61452, Korea. petruskim@chosun.ac.kr.
Youn Tae
YT
Kim
D000465:Algorithms; D002151:Calorimetry; D004734:Energy Metabolism / Q000502:physiology*; D006339:Heart Rate / Q000502:physiology; D016014:Linear Models; D016571:Neural Networks, Computer
context-based fuzzy c-means clustering; energy expenditure; linguistic regression; radial basis function neural network
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