An activity recording system with a radial-basis-function-network-based energy expenditure regression algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an activity recording (AR) system and a radial-basis-function-network-based (RBFNB) energy expenditure regression algorithm. The AR system includes motion sensors and an electrocardiogram sensor which is composed of a set of sensor modules (accelerometers and electrocardiogram amplifying/filtering circuits), a MCU module (microcontroller), a wireless communication module (a RF transceiver and a Bluetooth® module), and a storage module (flash memory). A RBFNB energy expenditure regression algorithm consisting of the procedures of data collection, data preprocessing, feature selection, and construction of energy expenditure regression model, has been developed for constructing energy expenditure regression models. The sequential forward search and the sequential backward search were employed as the feature selection strategies, and a radial basis function network as the energy expenditure regression model in this study. Our experimental results exhibited that the proposed energy expenditure regression algorithm can achieve satisfactory energy expenditure estimation by combing appropriate feature selection technique with the regression models.

Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011
Pages980-983
Number of pages4
Volume2
Publication statusPublished - 2011
Event2011 International Conference on Artificial Intelligence, ICAI 2011 - Las Vegas, NV, United States
Duration: 2011 Jul 182011 Jul 21

Other

Other2011 International Conference on Artificial Intelligence, ICAI 2011
CountryUnited States
CityLas Vegas, NV
Period11-07-1811-07-21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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