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dc.contributor.author Ahn, Jungmo -
dc.contributor.author Ra, Ho-Kyeong -
dc.contributor.author Yoon, Hee Jung -
dc.contributor.author Son, Sang Hyuk -
dc.contributor.author Ko, Jeonggil -
dc.date.accessioned 2021-01-22T07:37:45Z -
dc.date.available 2021-01-22T07:37:45Z -
dc.date.created 2020-11-19 -
dc.date.created 2020-11-19 -
dc.date.created 2020-11-19 -
dc.date.issued 2020-09 -
dc.identifier.citation IEEE Access, v.8, pp.184774 - 184784 -
dc.identifier.issn 2169-3536 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12820 -
dc.description.abstract The ubiquitous deployment of smart wearable devices brings promises for an effective implementation of various healthcare applications in our everyday living environments. However, given that these applications ask for accurate and reliable sensing results of vital signs, there is a need to understand the accuracy of commercial-off-the-shelf wearable devices’ healthcare sensing components (e.g., heart rate sensors). This work presents a thorough investigation on the accuracy of heart rate sensors equipped on three different widely used smartwatch platforms. We show that heart rate readings can easily diverge from the ground truth when users are actively moving. Moreover, we show that the accelerometer is not an effective secondary sensing modality of predicting the accuracy of such smartwatch-embedded sensors. Instead, we show that the photoplethysmography (PPG) sensor’s light intensity readings are an plausible indicator for determining the accuracy of optical sensor-based heart rate readings. Based on such observations, this work presents a light-weight Viterbi-algorithm-based Hidden Markov Model to design a filter that identifies reliable heart rate measurements using only the limited computational resources available on smartwatches. Our evaluations with data collected from four participants show that the accuracy of our proposed scheme can be as high as 98%. By enabling the smartwatch to self-filter misleading measurements from being healthcare application inputs, we see this work as an essential module for catalyzing novel ubiquitous healthcare applications. © 1991 BMJ Publishing Group. All rights reserved. -
dc.language English -
dc.publisher Institute of Electrical and Electronics Engineers Inc. -
dc.title On-Device Filter Design for Self-Identifying Inaccurate Heart Rate Readings on Wrist-Worn PPG Sensors -
dc.type Article -
dc.identifier.doi 10.1109/ACCESS.2020.3025776 -
dc.identifier.wosid 000583555800001 -
dc.identifier.scopusid 2-s2.0-85100290861 -
dc.type.local Article(Overseas) -
dc.type.rims ART -
dc.description.journalClass 1 -
dc.citation.publicationname IEEE Access -
dc.contributor.localauthor Ahn, Jungmo -
dc.contributor.localauthor Ra, Ho-Kyeong -
dc.contributor.localauthor Yoon, Hee Jung -
dc.contributor.localauthor Son, Sang Hyuk -
dc.contributor.localauthor Ko, Jeonggil -
dc.contributor.nonIdAuthor Ahn, Jungmo -
dc.contributor.nonIdAuthor Ra, Ho-Kyeong -
dc.contributor.nonIdAuthor Yoon, Hee Jung -
dc.contributor.nonIdAuthor Son, Sang Hyuk -
dc.contributor.nonIdAuthor Ko, Jeonggil -
dc.identifier.citationVolume 8 -
dc.identifier.citationStartPage 184774 -
dc.identifier.citationEndPage 184784 -
dc.identifier.citationTitle IEEE Access -
dc.type.journalArticle Article -
dc.description.isOpenAccess N -
dc.subject.keywordAuthor Heart rate monitoring -
dc.subject.keywordAuthor PPG sensor -
dc.subject.keywordAuthor reliable healthcare sensing -
dc.subject.keywordAuthor smartwatch -
dc.subject.keywordAuthor wearable devices -
dc.subject.keywordPlus MOTION ARTIFACTS -

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