Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Applicationopen access
- Authors
- Yoo, Sungwon; Ahmed, Shahzad; Kang, Sun; Hwang, Duhyun; Lee, Jungjun; Son, Jungduck; Cho, Sung Ho
- Issue Date
- Apr-2021
- Publisher
- MDPI
- Keywords
- vital sign monitoring; FMCW radar; smart sensor applications; GoogLeNet; deep learning
- Citation
- SENSORS, v.21, no.7, pp.1 - 16
- Indexed
- SCIE
SCOPUS
- Journal Title
- SENSORS
- Volume
- 21
- Number
- 7
- Start Page
- 1
- End Page
- 16
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1273
- DOI
- 10.3390/s21072412
- ISSN
- 1424-8220
- 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.
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Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
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