A non-contact camera-based method for respiratory rhythm extraction

Mateu-Mateus, Marc; Guede-Fernández, Federico; Rodríguez-Ibáñez, N.; García-González, M.A.; Ramos-Castro, Juan; Fernández-Chimeno, Mireya

doi:10.1016/j.bspc.2021.102443

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…Camera-based methods find interesting applications even outside the clinical scenarios. Of particular interest is the study of Mateu-Mateus et al (2021) who proposed a video-based method to record respiratory activity by detecting the movement of three small patterns attached on a seatbelt fastened by the subject for detecting drowsiness while driving in a simulated cabin car. Comparing the proposed method with the reference system (i.e., respiratory chest belt), a mean percentage absolute error (MAPE) lower than 4% for constrained respiration and a MAPE<6% for unconstrained respiration are reported.…”

Section: Monitoring Of Physiological Parameters and Possible Fields O...mentioning

confidence: 99%

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

et al. 2022

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The measurement of physiological parameters is fundamental to assess the health status of an individual. The contactless monitoring of vital signs may provide benefits in various fields of application, from healthcare and clinical setting to occupational and sports scenarios. Recent research has been focused on the potentiality of camera-based systems working in the visible range (380–750 nm) for estimating vital signs by capturing subtle color changes or motions caused by physiological activities but invisible to human eyes. These quantities are typically extracted from videos framing some exposed body areas (e.g., face, torso, and hands) with adequate post-processing algorithms. In this review, we provided an overview of the physiological and technical aspects behind the estimation of vital signs like respiratory rate, heart rate, blood oxygen saturation, and blood pressure from digital images as well as the potential fields of application of these technologies. Per each vital sign, we provided the rationale for the measurement, a classification of the different techniques implemented for post-processing the original videos, and the main results obtained during various applications or in validation studies. The available evidence supports the premise of digital cameras as an unobtrusive and easy-to-use technology for physiological signs monitoring. Further research is needed to promote the advancements of the technology, allowing its application in a wide range of population and everyday life, fostering a biometrical holistic of the human body (BHOHB) approach.

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Section: Monitoring Of Physiological Parameters and Possible Fields O...mentioning

confidence: 99%

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

et al. 2022

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“…Time-of-Flight LIDAR sensors have also been proposed for seat detection [14]. Recently, a camera-based method for measuring breathing has been proposed in [15]. It is based on the detection of a reflective object attached to the belt.…”

Section: Related Workmentioning

confidence: 99%

Seat-Occupancy Detection System and Breathing Rate Monitoring Based on a Low-Cost mm-Wave Radar at 60 GHz

et al. 2021

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This work presents a seat-occupancy detection system based on a PCR (Pulsed Coherent Radar) at the unlicensed 60 GHz ISM band. The radar can measure distances with sub-millimeter resolution. Therefore, the system can detect the presence of people occupying the seats by measuring small movements of the body, such as those produced by breathing. In consequence, the system not only measures seatoccupancy but also breathing rate, which is estimated from the amplitude peaks after filtering the amplitude measured by the radar to remove the noise. The effect of the car vibrations and random body movement is experimentally studied. Measurements performed with adults, as well as with a baby emulator sitting in a child seat, are presented. Comparison of the system with another that measures the breath based on airflow shows good agreement and permits its validation.

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“…1) FLANN Method: The Fast Library Approximated Nearest Neighbor (FLANN) method is used for finding the value of nearest neighbor [28,29]. Descriptors are produced by SIFT algorithm that is 128 dimensions for each keypoint while the SURF algorithm has 64 dimensions descriptor.…”

Section: G Matching Feature Query Image With Keyframementioning

confidence: 99%

Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm

Adhinata¹,

Harjoko²,

Wahyono³

2021

International Journal on Advanced Science, Engineering and Information Technology

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The pre-processing and feature extraction stages are the primary stages in object searching on video data. Processing video in all frames is inefficient. Frames that have the same information should only be once processed to the next stage. Then, the feature extraction algorithm that is often used to process video frames is SIFT and SURF. The SIFT algorithm is very accurate but slow. On the other hand, the SURF algorithm is fast but less accurate. Therefore, the requirement for keyframe selection and feature extraction methods is fast and accurate in object searching on real-time video. Video is pre-processed by extracting video into frames. Then, the mutual information entropy method is used for keyframe selection. Keyframes are extracted using the ORB algorithm. The multiple object detection in the video is done by clustering on features. The feature extraction results on each cluster are matched with the results of the feature from the query image. Matching results from keyframe on video with the query image is used to retrieve the video's frame information. The experiment shows that keyframe selection is beneficial in real-time video data processing because the keyframe selection speed is faster than feature extraction on each frame. Then, feature extraction using the ORB algorithm results 2 times faster speed results than SIFT and SURF algorithms with values not so different from SIFT algorithm. This study's results can be developed as a security warning system in public places, especially by security in providing evidence of criminal cases from videos.

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A non-contact camera-based method for respiratory rhythm extraction

Cited by 16 publications

References 22 publications

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

Seat-Occupancy Detection System and Breathing Rate Monitoring Based on a Low-Cost mm-Wave Radar at 60 GHz

Object Searching on Real-Time Video Using Oriented FAST and Rotated BRIEF Algorithm

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