Revisiting motion-based respiration measurement from videos

Qi Zhan, Jingjing Hu, Zitong Yu, Xiaobai Li, Wenjin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)


Video-based motion analysis gave rise to contactless respiration rate monitoring that measures subtle respiratory movement from a human chest or belly. In this paper, we revisit this technology via a large video benchmark that includes six categories of practical challenges. We analyze two video properties (i.e. pixel intensity variation and pixel movement) that are essential for respiratory motion analysis and various signal extraction approaches (i.e. from conventional to recent Convolutional Neural Network (CNN)-based methods). We find that pixel movement can better quantify respiratory motion than pixel intensity variation in various conditions. We also conclude that the simple conventional approach (e.g. Zerophase Component Analysis) can achieve better performance than CNN that uses data training to define the extraction of respiration signal, which thus raises a more general question whether CNN can improve video-based physiological signal measurement.

Original languageEnglish
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781728119908
Publication statusPublished - Jul 2020
Event42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: 20 Jul 202024 Jul 2020
Conference number: 42


Conference42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Abbreviated titleEMBC 2020


  • Humans
  • Motion
  • Movement
  • Neural Networks, Computer
  • Respiration
  • Respiratory Rate


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