A PACE sensor system with machine learning-based energy expenditure regression algorithm

Jeen Shing Wang, Che Wei Lin, Ya Ting C. Yang, Tzu Ping Kao, Wei Hsin Wang, Yen Shiun Chen

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
頁面529-536
頁數8
DOIs
出版狀態Published - 2011 十二月 1
事件7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
持續時間: 2011 八月 112011 八月 14

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6840 LNBI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other7th International Conference on Intelligent Computing, ICIC 2011
國家China
城市Zhengzhou
期間11-08-1111-08-14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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