Video-based respiration monitoring with automatic region of interest detection

Abstract

Abstract. Vital signs monitoring is ubiquitous in clinical environments and emerging in home-based healthcare applications. Still, since current monitoring methods require uncomfortable sensors, respiration rate remains the least measured vital sign. In this paper, we propose a video-based respiration monitoring method that automatically detects respiratory Region of Interest (RoI) and signal using a camera. Based on the observation that respiration induced chest/abdomen motion is an independent motion system in a video, our basic idea is to exploit the intrinsic properties of respiration to find the respiratory RoI and extract the respiratory signal via motion factorization. We created a benchmark dataset containing 148 video sequences obtained on adults under challenging conditions and also neonates in the neonatal intensive care unit (NICU). The measurements obtained by the proposed video respiration monitoring (VRM) method are not significantly different from the reference methods (guided breathing or contact-based ECG; p-value=0.6), and explain more than 99% of the variance of the reference values with low limits of agreement (−2.67 to 2.81 bpm). VRM seems to provide a valid solution to ECG in confined motion scenarios, though precision may be reduced for neonates. More studies are needed to validate VRM under challenging recording conditions, including upper-body motion types.

Contactless Monitoring of Breathing Patterns and Respiratory Rate at the Pit of the Neck: A Single Camera Approach

“…Several attempts have been proposed to extract respiratory features from video frames recording breathing-related movements of thorax [8][9][10], thoracoabdominal area [8,11], face area [12]- [14], and area at the edge of the shoulder [15]. Even though some studies consider region of interest (ROI) which include the neck region [14], none specifically considers the pit of the neck that is a large, visible dip in between the neck and the two collarbones that may be easily identifiable from the video.…”

“…Different approaches have been also used to postprocess the pixel data to extract signal related to the respiration from such videos by the subtraction of two continuous images [8,11], analysis of pixel intensity changes based upon independent component analysis [12,13], analysis of average contributions of red, green, and blue channel of the video [14,16,17], and analysis of optical flow [9]. Even though breathing patterns and respiratory rates have been faithfully estimated using high-quality cameras [14,16], several other approaches that rely on off-the-shelf webcams also are able to achieve the same level of monitoring accuracy [7,8,12,13].…”

Remote patient monitoring: a comprehensive study

“…In paper (Alzahrani and Whitehead 2015), a similar approach is discussed but this system has Eulerian video magnification and face tracking. The research described in Janssen et al (2016) measures thoracic impedance plethysmography using Phillips Intellivue MP50 and CCD-RGB camera. This system has automatic region-of-interest (ROI) detection.…”

A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

“…The contribution of this work is that the final proposed system does not require the selection of a ROI as others methods have reported in the literature [18,20,[23][24][25][26]34]. In addition, to the best of our knowledge, this is the first time that a CNN is trained using tagged frames as inhalation and exhalation, instead of a raw respiratory rate signal used to train other CNN strategies [18].…”