Abstract
The respiratory rate is an important vital parameter that provides information about persons’ physical condition. In clinical practice it is currently only monitored using contact-based techniques, which can have negative effects on patients. In this study, a new algorithm for remote respiratory rate recognition is presented using photoplethysmographic signals derived from facial video images in the visible light spectrum. The effects of different implementation steps in the presented algorithm are investigated in order to optimize the approach and gain new findings in this research field. In addition, a detailed examination of already implemented procedures is performed and the results are compared on two different databases. We show that by fusing the results of seven different respiratory-induced modulations in combination with other processing steps, very good estimates for the respiratory rate on both moving and non-moving data are achieved. The obtained detection rates of 72.16 % and 87.68 % are significantly higher than those of the best comparison algorithm with 37.37 % and 59.13 %. The comparison algorithms developed so far are not competitive with the newly designed method, especially for video recordings involving persons in motion. This paper provides important new findings in the field of facial video-based respiratory rate recognition for the research community. A new method has been created that delivers significantly better estimates of the respiratory rate than previously developed techniques.
Highlights
The respiratory rate (RR) is an important diagnostic parameter that can provide information about persons’ physical condition, notably because it contains prognostic information and can point to initial indications of a later case of illness [1]
Suitable databases are rare and difficult to find. They must meet certain standards for the application of non-contact vital signs monitoring, such as a low level of video compression, because the small color changes that are required for the heart rate (HR) detection can be reduced or even completely eliminated [47]
The results show a clear improvement in detection performance for hue, chrominance-based approach (CHROM) and Normalized green (normG) compared to the G channel
Summary
The respiratory rate (RR) is an important diagnostic parameter that can provide information about persons’ physical condition, notably because it contains prognostic information and can point to initial indications of a later case of illness [1]. It serves hospitals as a highly sensitive value which is capable of mapping a patient’s state of health [2]. It is often measured via the sensors of an electrocardiogram on the test person’s upper body or via photoplethysmography (PPG) techniques, e.g. a finger pulse oximeter.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
