Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application

Clicks: 254
ID: 268669
2021
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #975 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
yoo2021sensorsradar Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sungwon Yoo;Shahzad Ahmed;Sun Kang;Duhyun Hwang;Jungjun Lee;Jungduck Son;Sung Ho Cho;Yoo, Sungwon;Ahmed, Shahzad;Kang, Sun;Hwang, Duhyun;Lee, Jungjun;Son, Jungduck;Cho, Sung Ho;
Journal sensors
Year 2021
DOI
10.3390/s21072412
URL
https://doi.org/10.3390/s21072412
Keywords
Deep learning fmcw radar vital sign monitoring smart sensor applications googlenet

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.