Image-Based Motion-Tolerant Remote Respiratory Rate Evaluation

Kuan Yi Lin, Duan Yu Chen, W. J. Tsai

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Contact measurements of the respiratory rate using conventional electrocardiogram equipment requires patients to wear chest straps that can cause skin irritation and discomfort. Therefore, a real-time robust non-contact technique is developed for the measurement of respiratory rate variation. The changes of a simple harmonic motion between inhalation and exhalation from the human's upper body can be observed from visual appearance. Therefore, in this paper, to characterize the motion, a salient region is automatically selected in the energy map resulted from Haar-like features through consecutive frames. Furthermore, a median optical flow signal is used to acquire the primary respiratory rate signal. The effective respiratory frequencies resulted from vertical motion variation are decomposed by median motion signal and then characterized by zero-crossing method before the frequencies were rectified by an estimation using the noise elimination methods. In the experiment, the performance is evaluated using an extensive data set obtained under distinct four respiratory types, regular respiration, respiration with body motion, respiration with distinct poses, and respiration with distinct capturing distances. Our approach develops a convenient non-contact method to evaluate the respiratory rate with the achievement of measurement under farther distance and also outperforms the current state-of-the-art approach in terms of high correlation coefficient. Therefore, the experiment results show its efficacy for real-world environment.

Original languageEnglish
Article number7400915
Pages (from-to)3263-3271
Number of pages9
JournalIEEE Sensors Journal
Volume16
Issue number9
DOIs
StatePublished - 1 May 2016

Fingerprint Dive into the research topics of 'Image-Based Motion-Tolerant Remote Respiratory Rate Evaluation'. Together they form a unique fingerprint.

Cite this