Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, Sungwon | - |
dc.contributor.author | Ahmed, Shahzad | - |
dc.contributor.author | Kang, Sun | - |
dc.contributor.author | Hwang, Duhyun | - |
dc.contributor.author | Lee, Jungjun | - |
dc.contributor.author | Son, Jungduck | - |
dc.contributor.author | Cho, Sung Ho | - |
dc.date.accessioned | 2021-07-30T04:45:11Z | - |
dc.date.available | 2021-07-30T04:45:11Z | - |
dc.date.created | 2021-07-14 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1273 | - |
dc.description.abstract | The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Cho, Sung Ho | - |
dc.identifier.doi | 10.3390/s21072412 | - |
dc.identifier.scopusid | 2-s2.0-85103243656 | - |
dc.identifier.wosid | 000638871100001 | - |
dc.identifier.bibliographicCitation | SENSORS, v.21, no.7, pp.1 - 16 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 21 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 16 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | SENSORS | - |
dc.subject.keywordAuthor | vital sign monitoring | - |
dc.subject.keywordAuthor | FMCW radar | - |
dc.subject.keywordAuthor | smart sensor applications | - |
dc.subject.keywordAuthor | GoogLeNet | - |
dc.subject.keywordAuthor | deep learning | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/21/7/2412 | - |
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