Q. Zhan, J. Hu, Z. Yu, X. Li and W. Wang, "Revisiting motion-based respiration measurement from videos," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 5909-5912, doi: 10.1109/EMBC44109.2020.9175662
Revisiting motion-based respiration measurement from videos
|Author:||Zhan, Qi1; Hu, Jingjing1; Yu, Zitong2;|
1College of Electrical and Information Engineering, Hunan University, Changsha 410082, People’s Republic of China
2Center for Machine Vision and Signal Analysis, University of Oulu, FI-90014, Finland
3indhoven University of Technology, The Netherlands
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020110689602
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-11-06
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.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
|Pages:||5909 - 5912|
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This research is supported by the National Natural Science Foundation of China (Grant No. 61671204).
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