A Multi-step Heart Rate Prediction Method based on Physical Activity using Adams-Bashforth Technique

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Title A Multi-step Heart Rate Prediction Method based on Physical Activity using Adams-Bashforth Technique
Author Xiao, Feng; Yuchi, Ming; Ding, Ming-yue; Jo, Jun Hyung; Kim, Jong-Hwan
Publication Title 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation: Proceedings
Editor Jong-Hwan Kim
Year Published 2009
Place of publication Incheon, Korea
Publisher CIRAS
Abstract Physical activity (PA) is commonly recognized to directly influence changes in heart rate (HR). HR prediction based on PA can be a useful tool in medical research and monitoring in a clinical setting. In our previous works, predictors with high accuracy were designed. However, the HR could only be predicted in single time steps. In this study, a multi-step HR prediction method is proposed. Firstly, the HR prediction problem was converted into an Initial-Value Problem for Ordinary Differential Equation (IVPODE). Then the Adams-Bashforth method was used to implement multi-step prediction. Only HR at the initial time step and PA signals are needed. Experiments were conducted based on the real-life signals from a healthy male. Predicted HR can approximately trace the actual HR in a long time step. The results show the potential of this proposed method.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/CIRA.2009.5423181
Copyright Statement Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 9781424448098
Conference name CIRCA2009
Location Daejeon, Korea
Date From 2009-12-15
Date To 2009-12-18
URI http://hdl.handle.net/10072/31898
Date Accessioned 2010-03-08
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Neural, Evolutionary and Fuzzy Computation
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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