CNNs for heart rate estimation and human activity recognition in wrist worn sensing applications

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dc.contributor.authorBrophy, Eoin
dc.contributor.authorMuehlhausen, Willie
dc.contributor.authorSmeaton, Alan F.
dc.contributor.authorWard, Tomás E.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2020-10-30T15:56:14Z
dc.date.available2020-10-30T15:56:14Z
dc.date.issued2020-03
dc.description.abstractWrist-worn smart devices are providing increased insights into human health, behaviour and performance through sophisticated analytics. However, battery life, device cost and sensor performance in the face of movement-related artefact present challenges which must be further addressed to see effective applications and wider adoption through commoditisation of the technology. We address these challenges by demonstrating, through using a simple optical measurement, photoplethysmography (PPG) used conventionally for heart rate detection in wrist-worn sensors, that we can provide improved heart rate and human activity recognition (HAR) simultaneously at low sample rates, without an inertial measurement unit. This simplifies hardware design and reduces costs and power budgets. We apply two deep learning pipelines, one for human activity recognition and one for heart rate estimation. HAR is achieved through the application of a visual classification approach, capable of robust performance at low sample rates. Here, transfer learning is leveraged to retrain a convolutional neural network (CNN) to distinguish characteristics of the PPG during different human activities. For heart rate estimation we use a CNN adopted for regression which maps noisy optical signals to heart rate estimates. In both cases, comparisons are made with leading conventional approaches. Our results demonstrate a low sampling frequency can achieve good performance without significant degradation of accuracy. 5 Hz and 10 Hz were shown to have 80.2% and 83.0% classification accuracy for HAR respectively. These same sampling frequencies also yielded a robust heart rate estimation which was comparative with that achieved at the more energy-intensive rate of 256 Hz.en
dc.description.sponsorshipScience Foundation (Grant nos.17/RC-PhD/3482, SFI/12/RC/2289 and SAP SE)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBrophy, E., Muehlhausen, W., Smeaton, A. F. and Ward, T. E. (2020) 'CNNs for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications'. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, 23-27 March, pp. 1-6. doi: 10.1109/PerComWorkshops48775.2020.9156120en
dc.identifier.doi10.1109/PerComWorkshops48775.2020.9156120en
dc.identifier.endpage6en
dc.identifier.isbn978-1-7281-4716-1
dc.identifier.isbn978-1-7281-4717-8
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/10689
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://ieeexplore.ieee.org/document/9156120
dc.rights© 2020 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 worksen
dc.subjectDeep learningen
dc.subjectTransfer learningen
dc.subjectPhotoplethysmographyen
dc.subjectHeart rateen
dc.subjectEstimationen
dc.subjectActivity recognitionen
dc.subjectFeature extractionen
dc.subjectMonitoringen
dc.subjectTrainingen
dc.subjectMachine learningen
dc.titleCNNs for heart rate estimation and human activity recognition in wrist worn sensing applicationsen
dc.typeConference itemen
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