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Mobile Health Monitoring System Including Biofeedback Training Through Analysis of PPG and Respiratory Pattern Change

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dc.contributor.authorKim, D.-
dc.contributor.authorKim, J.-
dc.contributor.authorJeong, J.-
dc.contributor.authorKim, S.-
dc.date.accessioned2023-04-28T00:41:07Z-
dc.date.available2023-04-28T00:41:07Z-
dc.date.issued2020-
dc.identifier.issn1876-1100-
dc.identifier.issn1876-1119-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/7086-
dc.description.abstractFree radical oxygen generated by stress and oxygen deficiency causes problems such as heart disease and stroke. We propose mobile health monitoring system to solve this problem. Based on biofeedback technology used in psychiatry, meaningful parameters were identified by analyzing the autonomic nervous system and breathing patterns that change during deep breathing. As a result, PP-interval was increased (p < 0.05) during deep breathing and the average expiration value of the subject was unconsciously increased (p < 0.05). The deep breathing has been shown to be able to prevent stress and oxygen deficiency. After that, we developed an effective mobile health monitoring system using parameter that have significant result value. If personal health monitoring system that continuously induces deep breathing through the method of this study is developed, it can be used as a way to continuously manage and improve individual health. © 2020, Springer Nature Singapore Pte Ltd.-
dc.format.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisherSpringer-
dc.titleMobile Health Monitoring System Including Biofeedback Training Through Analysis of PPG and Respiratory Pattern Change-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.doi10.1007/978-981-13-9341-9_39-
dc.identifier.scopusid2-s2.0-85076833753-
dc.identifier.bibliographicCitationLecture Notes in Electrical Engineering, v.536 LNEE, pp 228 - 233-
dc.citation.titleLecture Notes in Electrical Engineering-
dc.citation.volume536 LNEE-
dc.citation.startPage228-
dc.citation.endPage233-
dc.type.docTypeConference Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorBiofeedback-
dc.subject.keywordAuthorBreathing pattern-
dc.subject.keywordAuthorPP-interval-
dc.subject.keywordAuthorUbiquitous system-
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