Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application
| Autor: | Duhyun Hwang, Jungduck Son, Sun Kang, Sungwon Yoo, Jungjun Lee, Shahzad Ahmed, Sung Ho Cho |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Vital signs smart sensor applications 02 engineering and technology FMCW radar lcsh:Chemical technology Machine learning computer.software_genre 01 natural sciences Biochemistry Radar systems Article Analytical Chemistry law.invention law 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Radar Instrumentation Vital sign monitoring business.industry Deep learning Child safety 010401 analytical chemistry Sign (semiotics) deep learning 020206 networking & telecommunications Atomic and Molecular Physics and Optics 0104 chemical sciences Continuous-wave radar vital sign monitoring Artificial intelligence business computer GoogLeNet |
| Zdroj: | Sensors Volume 21 Issue 7 Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 2412, p 2412 (2021) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21072412 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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