Detailed Information

Cited 2 time in webofscience Cited 5 time in scopus
Metadata Downloads

Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror

Full metadata record
DC Field Value Language
dc.contributor.authorYang, Bo-Seung-
dc.contributor.authorKang, Tae-Won-
dc.contributor.authorChoi, Yong-Sik-
dc.contributor.authorJung, Jin-Woo-
dc.date.accessioned2023-04-27T10:40:55Z-
dc.date.available2023-04-27T10:40:55Z-
dc.date.issued2022-07-
dc.identifier.issn2076-3417-
dc.identifier.issn2076-3417-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/2929-
dc.description.abstractAs the elderly population increases globally, the demand for systems and algorithms that target the elderly is increasing. Focusing on the extendibility of smart mirrors, our purpose is to create a motion detection system based on video input by an attached device (an RGB camera). The motion detection system presented in this paper is based on an algorithm that returns a Boolean value indicating the detection of motion based on skeletal information. We analyzed the problems that occur when the adjacent frame subtraction method (AFSM) is used in the motion detection algorithm based on the skeleton-related output of the pose estimation model. We compared and tested the motion recognition rate for slow-motion with the previously used AFSM and the vector sum method (VSM) proposed in this paper. As an experimental result, the slow-motion detection rate showed an increase of 30-70%.-
dc.format.extent14-
dc.language영어-
dc.language.isoENG-
dc.publisherMDPI-
dc.titleError-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/app12147024-
dc.identifier.scopusid2-s2.0-85137321580-
dc.identifier.wosid000831523000001-
dc.identifier.bibliographicCitationApplied Sciences, v.12, no.14, pp 1 - 14-
dc.citation.titleApplied Sciences-
dc.citation.volume12-
dc.citation.number14-
dc.citation.startPage1-
dc.citation.endPage14-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryEngineering, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordAuthorslow-motion detection-
dc.subject.keywordAuthorpose estimation model-
dc.subject.keywordAuthorsmart mirror-
dc.subject.keywordAuthorelder-friendly-
dc.subject.keywordAuthorartificial intelligence-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Advanced Convergence Engineering > Department of Computer Science and Artificial Intelligence > 1. Journal Articles

qrcode

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

Related Researcher

Researcher Jung, Jin Woo photo

Jung, Jin Woo
College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)
Read more

Altmetrics

Total Views & Downloads

BROWSE