Skip navigation
Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/51969
Full metadata record
DC FieldValueLanguage
dc.contributor.authorQiu, G. W.-
dc.coverage.spatialМинскru_RU
dc.date.accessioned2023-06-13T10:55:26Z-
dc.date.available2023-06-13T10:55:26Z-
dc.date.issued2023-
dc.identifier.citationQiu, G. W. Heart rate estimation from photoplethysmogram and accleration smartphone data based on convolutional neuralnetwork and long short time memory network / G. W. Qiu // Информационная безопасность : сборник материалов 59-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 17–21 апреля 2023 г. / Белорусский государственный университет информатики и радиоэлектроники. – Минск, 2023. – С. 165–167.ru_RU
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/51969-
dc.description.abstractThe wearable reflective photocapacitance plethysmograph (PPG) sensor can be integrated into the watch or strap to provide instantaneous heart rate (HRs), causing minimal inconvenience to users. However, the existence of motion artifacts (MAs) leads to inaccurate heart rate estimation. In order to solve this problem, I propose a new deep learning neural network to ensure accurate estimation of HR in high-intensity exercise.The average absolute error of the algorithm for all training data sets and test data sets is less than 1.5 bpm, including 1.09 bpm for training data sets and 1.46 bpm for test data sets.ru_RU
dc.language.isoenru_RU
dc.publisherБГУИРru_RU
dc.subjectматериалы конференцийru_RU
dc.subjectConvolutionru_RU
dc.subjectConcatenationru_RU
dc.subjectHeart rate and motion artifactsru_RU
dc.titleHeart rate estimation from photoplethysmogram and accleration smartphone data based on convolutional neuralnetwork and long short time memory networkru_RU
dc.typeArticleru_RU
Appears in Collections:Информационная безопасность : материалы 59-й научной конференции аспирантов, магистрантов и студентов (2023)

Files in This Item:
File Description SizeFormat 
Qiu_Heart_rate.pdf146.9 kBAdobe PDFView/Open
Show simple item record Google Scholar

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.