Skip navigation
Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/51886
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWan, Z.-
dc.contributor.authorBaryskievic, A. A.-
dc.coverage.spatialМинскru_RU
dc.date.accessioned2023-06-08T12:01:06Z-
dc.date.available2023-06-08T12:01:06Z-
dc.date.issued2023-
dc.identifier.citationWan, Z. Human physical activity recognition algorithm based on smartphone data and convolutional neural network / Z. Wan, A. A. Baryskievic // Технологии передачи и обработки информации : материалы Международного научно-технического семинара, Минск, март-апрель 2023 г. / Белорусский государственный университет информатики и радиоэлектроники; редкол.: В. Ю. Цветков [и др.]. – Минск, 2023. – С. 72–77.ru_RU
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/51886-
dc.description.abstractWith a widespread of various sensors embedded in mobile devices, the analysis of human daily activities becomes more common and straightforward. Human activity recognition (HAR) is a prominent application of advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques that utilizes computer vision to understand the semantic meanings of heterogeneous human actions. This paper describes a supervised learning method that can distinguish human actions based on data collected from practical human movements. The primary challenge while working with HAR is to overcome the difficulties that come with the cyclostationary nature of the activity signals. This study proposes a HAR classification model based on a Convolutional Neural Network (CNN) and uses the collected human action signals. The model was tested on the WISDM dataset, which resulted in a 92 % classification accuracy. This approach will help to conduct further researches on the recognition of human activities based on their biomedical signals.ru_RU
dc.language.isoenru_RU
dc.publisherБГУИРru_RU
dc.subjectматериалы конференцийru_RU
dc.subjecthuman activity recognitionru_RU
dc.subjectartificial inteligenceru_RU
dc.subjectmachine learningru_RU
dc.titleHuman physical activity recognition algorithm based on smartphone data and convolutional neural networkru_RU
dc.typeArticleru_RU
Appears in Collections:Технологии передачи и обработки информации : материалы Международного научно-технического семинара (2023)

Files in This Item:
File Description SizeFormat 
Wan_Human.pdf549.43 kBAdobe PDFView/Open
Show simple item record Google Scholar

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