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Sensors 2016, 16(10), 1566; doi:10.3390/s16101566

Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network

1
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
2
Department of Electronics Engineering, Chosun University, Gwangju 61452, Korea
3
Department of IT Fusion Technology, Graduate School, Chosun University, Gwangju 61452, Korea
*
Authors to whom correspondence should be addressed.
Academic Editor: Davide Brunelli
Received: 13 June 2016 / Revised: 8 September 2016 / Accepted: 20 September 2016 / Published: 22 September 2016
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
View Full-Text   |   Download PDF [2145 KB, uploaded 22 September 2016]   |  

Abstract

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. View Full-Text
Keywords: energy expenditure; linguistic regression; radial basis function neural network; context-based fuzzy c-means clustering energy expenditure; linguistic regression; radial basis function neural network; context-based fuzzy c-means clustering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, M.; Kwak, K.-C.; Kim, Y.T. Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network. Sensors 2016, 16, 1566.

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