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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011
頁面980-983
頁數4
2
出版狀態Published - 2011
事件2011 International Conference on Artificial Intelligence, ICAI 2011 - Las Vegas, NV, United States
持續時間: 2011 七月 182011 七月 21

Other

Other2011 International Conference on Artificial Intelligence, ICAI 2011
國家United States
城市Las Vegas, NV
期間11-07-1811-07-21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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