TY - GEN
T1 - A PACE sensor system with machine learning-based energy expenditure regression algorithm
AU - Wang, Jeen Shing
AU - Lin, Che Wei
AU - Yang, Ya Ting C.
AU - Kao, Tzu Ping
AU - Wang, Wei Hsin
AU - Chen, Yen Shiun
PY - 2011
Y1 - 2011
N2 - This paper presents a portable-accelerometer and electrocardiogram (PACE) sensor system and a machine learning-based energy expenditure regression algorithm. The PACE sensor system includes motion sensors and an electrocardiogram sensor, a MCU module (microcontroller), a wireless communication module (a RF transceiver and a Bluetooth® module), and a storage module (flash memory). A machine learning-based 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 in this study. The sequential forward search and the sequential backward search were employed as the feature selection strategies, and a generalized regression neural network were employed as the energy expenditure regression models in this study. Our experimental results exhibited that the proposed machine learning-based energy expenditure regression algorithm can achieve satisfactory energy expenditure estimation by combing appropriate feature selection technique with machine learning-based regression models.
AB - This paper presents a portable-accelerometer and electrocardiogram (PACE) sensor system and a machine learning-based energy expenditure regression algorithm. The PACE sensor system includes motion sensors and an electrocardiogram sensor, a MCU module (microcontroller), a wireless communication module (a RF transceiver and a Bluetooth® module), and a storage module (flash memory). A machine learning-based 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 in this study. The sequential forward search and the sequential backward search were employed as the feature selection strategies, and a generalized regression neural network were employed as the energy expenditure regression models in this study. Our experimental results exhibited that the proposed machine learning-based energy expenditure regression algorithm can achieve satisfactory energy expenditure estimation by combing appropriate feature selection technique with machine learning-based regression models.
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U2 - 10.1007/978-3-642-24553-4_70
DO - 10.1007/978-3-642-24553-4_70
M3 - Conference contribution
AN - SCOPUS:84862963676
SN - 9783642245527
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 529
EP - 536
BT - Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
T2 - 7th International Conference on Intelligent Computing, ICIC 2011
Y2 - 11 August 2011 through 14 August 2011
ER -