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Title: PARHELIA: Particle Filter-Based Heart Rate Estimation from Photoplethysmographic Signals during Physical Exercise
Authors: Fujita, Yuya
Hiromoto, Masayuki  kyouindb  KAKEN_id
Sato, Takashi  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 廣本, 正之
Keywords: Heart rate estimation
motion artifact removal
particle filter
photoplethysmography (PPG)
Issue Date: Jan-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Journal title: IEEE Transactions on Biomedical Engineering
Volume: 65
Issue: 1
Start page: 189
End page: 198
Abstract: The photoplethysmographic (PPG) signal is an important source of information for estimating heart rate (HR). However, the PPG signal could be strongly contaminated by the motion artifact (MA) of the subjects, making HR estimation a particularly difficult problem. In this paper, we propose PARHELIA, a PARticle filter-based algorithm for HEart rate estimation using photopLethysmographIc signAls. The proposed method employs a particle filter, and utilizes the simultaneously recorded acceleration signals from a wrist-type sensor, to keep track of multiple HR candidates. This achieves quick recovery from incorrect HR estimations under the strong influence of the MA. Experimental results for a dataset of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.17 beats per minute (BPM) whereas that of the bestknown conventional method, JOSS, is 1.28 BPM. Furthermore, the estimation time of PARHELIA is 20 times shorter than JOSS.
Rights: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
This is not the published version. Please cite only the published version.
DOI(Published Version): 10.1109/TBME.2017.2697911
Appears in Collections:Journal Articles

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