Abstract
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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Wang, JS., Lin, CW., Yang, YT.C., Kao, TP., Wang, WH., Chen, YS. (2012). A PACE Sensor System with Machine Learning-Based Energy Expenditure Regression Algorithm. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_70
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DOI: https://doi.org/10.1007/978-3-642-24553-4_70
Publisher Name: Springer, Berlin, Heidelberg
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