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Video Analysis for Health Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 16587

Special Issue Editors


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Guest Editor
ImViA-EA7535, Univ. Bourgogne Franche-Comté, 21000 Dijon, France
Interests: biomedical engineering; affective computing; image processing; video analytics

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Guest Editor
Laboratoire de Conception, Optimisation et Modélisation des Systèmes, LCOMS EA 7306, Université de Lorraine, 57000 Metz, France
Interests: biomedical engineering; affective computing; image processing; artificial intelligence

Special Issue Information

Dear Colleagues,

The use of cameras with advanced analysis methods has recently emerged as a promising alternative to conventional contact medical sensors attached to the patient's body. Because of its non-contact nature, this technology will significantly improve patient comfort and pave the way for many new applications. In this special issue, we welcome contributions on methods and systems for measuring physiological signals using cameras and their applications: health monitoring (e.g., during sleep or in fitness and driving contexts); affective computing; and security applications (e.g., video surveillance, biometry, or anti-spoofing). Contributions related to physiological signal measurement from unconventional cameras (multispectral cameras, NIR, and thermal cameras) will also be considered.

Prof. Dr. Yannick Benezeth
Prof. Dr. Frédéric Bousefsaf
Guest Editors

Manuscript Submission Information

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Keywords

  • methods for extracting physiological signals from videos;
  • camera-based systems for health monitoring;
  • applications of video health monitoring (sleep monitoring, fitness cardio-training, driver monitoring, etc.);
  • video-based physiological measurement for affective computing;
  • video-based physiological measurement for security applications (video surveillance, biometry, and anti-spoofing);
  • Unconventional cameras for sensing physiological signals (multispectral cameras, NIR, thermal cameras, etc.)

Published Papers (3 papers)

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Research

25 pages, 921 KiB  
Article
A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
by Théo Desquins, Frédéric Bousefsaf, Alain Pruski and Choubeila Maaoui
Appl. Sci. 2022, 12(19), 9582; https://doi.org/10.3390/app12199582 - 23 Sep 2022
Cited by 13 | Viewed by 4079
Abstract
Photoplethysmography is a method to visualize the variation in blood volume within tissues with light. The signal obtained has been used for the monitoring of patients, interpretation for diagnosis or for extracting other physiological variables (e.g., pulse rate and blood oxygen saturation). However, [...] Read more.
Photoplethysmography is a method to visualize the variation in blood volume within tissues with light. The signal obtained has been used for the monitoring of patients, interpretation for diagnosis or for extracting other physiological variables (e.g., pulse rate and blood oxygen saturation). However, the photoplethysmography signal can be perturbed by external and physiological factors. Implementing methods to evaluate the quality of the signal allows one to avoid misinterpretation while maintaining the performance of its applications. This paper provides an overview on signal quality index algorithms applied to photoplethysmography. We try to provide a clear view on the role of a quality index and its design. Then, we discuss the challenges arising in the quality assessment of imaging photoplethysmography. Full article
(This article belongs to the Special Issue Video Analysis for Health Monitoring)
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17 pages, 1825 KiB  
Article
Performance Evaluation of rPPG Approaches with and without the Region-of-Interest Localization Step
by Žan Pirnar, Miha Finžgar and Primož Podržaj
Appl. Sci. 2021, 11(8), 3467; https://doi.org/10.3390/app11083467 - 13 Apr 2021
Cited by 4 | Viewed by 2408
Abstract
Traditionally, the first step in physiological measurements based on remote photoplethysmography (rPPG) is localizing the region of interest (ROI) that contains a desired pulsatile information. Recently, approaches that do not require this step have been proposed. The purpose of this study was to [...] Read more.
Traditionally, the first step in physiological measurements based on remote photoplethysmography (rPPG) is localizing the region of interest (ROI) that contains a desired pulsatile information. Recently, approaches that do not require this step have been proposed. The purpose of this study was to evaluate the performance of selected approaches with and without ROI localization step in rPPG signal extraction. The Viola-Jones face detector and Kanade–Lucas–Tomasi tracker (VK) in combination with (a) a region-of-interest (ROI) cropping, (b) facial landmarks, (c) skin-color segmentation, and (d) skin detection based on maximization of mutual information and an approach without ROI localization step (Full Video Pulse (FVP)) were studied. Final rPPG signals were extracted using selected model-based and data-driven rPPG algorithms. The performance of the approaches was tested on three publicly available data sets offering compressed and uncompressed video recordings covering various scenarios. The success rates of pulse waveform signal extraction range from 88.37% (VK with skin-color segmentation) to 100% (FVP). In challenging scenarios (skin tone, lighting conditions, exercise), there were no statistically significant differences between the studied approaches in terms of SNR. The best overall performance in terms of RMSE was achieved by a combination of VK with ROI cropping and the model-based rPPG algorithm. Results indicate that the selection of the ROI localization approach does not significantly affect rPPG measurements if combined with a robust algorithm for rPPG signal extraction. Full article
(This article belongs to the Special Issue Video Analysis for Health Monitoring)
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24 pages, 2102 KiB  
Article
Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation
by Amogh Gudi, Marian Bittner and Jan van Gemert
Appl. Sci. 2020, 10(23), 8630; https://doi.org/10.3390/app10238630 - 2 Dec 2020
Cited by 37 | Viewed by 9016
Abstract
Remote photo-plethysmography (rPPG) uses a camera to estimate a person’s heart rate (HR). Similar to how heart rate can provide useful information about a person’s vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is [...] Read more.
Remote photo-plethysmography (rPPG) uses a camera to estimate a person’s heart rate (HR). Similar to how heart rate can provide useful information about a person’s vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair. Full article
(This article belongs to the Special Issue Video Analysis for Health Monitoring)
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