Accurate wearable heart rate monitoring during physical exercises using PPG

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dc.contributor.author Temko, Andriy
dc.date.accessioned 2017-06-23T11:39:05Z
dc.date.available 2017-06-23T11:39:05Z
dc.date.issued 2017-03-01
dc.identifier.citation Temko, A. (2017) 'Accurate Wearable Heart Rate Monitoring During Physical Exercises Using PPG', IEEE Transactions on Biomedical Engineering, In Press, doi: 10.1109/TBME.2017.2676243 en
dc.identifier.issn 0018-9294
dc.identifier.uri http://hdl.handle.net/10468/4185
dc.identifier.doi 10.1109/TBME.2017.2676243
dc.description.abstract Objective: The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises is tackled in this paper. Methods: The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive postprocessing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings. Results: On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with 2 existing algorithms. Conclusion: The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods. Significance: The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The Matlab implementation of the algorithm is provided online. en
dc.description.sponsorship Science Foundation Ireland (SFI Research Centre Award (12/RC/2272)) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher IEEE en
dc.rights © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. en
dc.subject Accelerometers en
dc.subject Algorithm design and analysis en
dc.subject Estimation en
dc.subject Heart rate en
dc.subject Monitoring en
dc.subject Vocoders en
dc.subject Wiener filters en
dc.subject Photoplethysmographic en
dc.subject Viterbi decoding en
dc.subject Wiener filter en
dc.subject Phase vocoder en
dc.subject Spectrum estimation en
dc.title Accurate wearable heart rate monitoring during physical exercises using PPG en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Andriy Temko, Electrical & Electronic Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: andreyt@eleceng.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2017-06-23T11:21:54Z
dc.description.version Accepted Version en
dc.internal.rssid 400214314
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle IEEE Transactions On Biomedical Engineering en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress andreyt@eleceng.ucc.ie en
dc.internal.bibliocheck Accepted Version online June 2017. Check and update citation details. Add volume, start page, end page


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