Rollover Detection of Infants Using Posture Estimation Model

Ayaka Okuno, Takaaki Ishikawa, Hiroshi Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In predicting the posture of an infant during sleep, images taken by a surveillance camera are useful. The posture estimation of infants is designed to be used in a home environment and uses monocular camera images rather than special cameras such as distance cameras. In this paper, we compare the accuracy of infant posture estimation by two posture estimation models, OpenPose and Cascaded Pyramid Network(CPN). We also introduce a system for estimating infant's sleep turn using the posture estimation results. The experimental results show that CPN can detect infants' posture estimation with higher accuracy than OpenPose. In addition, the system is successfully used to detect the infant's turning with high accuracy.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages490-493
Number of pages4
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
CountryJapan
CityKobe
Period20/10/1320/10/16

Keywords

  • CPN
  • deep learning
  • OpenPose
  • Sleep Behavior in Infants

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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