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3 pages, 157 KiB

Open AccessEditorial

Editorial for Special Issue: Contactless Vital Signs Monitoring

by Gerard de Haan and Wim Verkruysse

Appl. Sci. 2020, 10(1), 166; https://doi.org/10.3390/app10010166 - 24 Dec 2019

Cited by 2 | Viewed by 2251

Abstract

Cameras have changed our way of life in many ways [...] Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

Research

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11 pages, 1513 KiB

Open AccessArticle

Contactless Monitoring of Microcirculation Reaction on Local Temperature Changes

by Maxim A. Volynsky, Nikita B. Margaryants, Oleg V. Mamontov and Alexei A. Kamshilin

Appl. Sci. 2019, 9(22), 4947; https://doi.org/10.3390/app9224947 - 17 Nov 2019

Cited by 18 | Viewed by 2744

Abstract

Assessment of skin blood flow is an important clinical task which is required to study mechanisms of microcirculation regulation including thermoregulation. Contactless assessment of vasomotor reactivity in response to thermal exposure is currently not available. The aim of this study is to show [...] Read more.

Assessment of skin blood flow is an important clinical task which is required to study mechanisms of microcirculation regulation including thermoregulation. Contactless assessment of vasomotor reactivity in response to thermal exposure is currently not available. The aim of this study is to show the applicability of the imaging photoplethysmography (IPPG) method to measure quantitatively the vasomotor response to local thermal exposure. Seventeen healthy subjects aged 23 ± 7 years participated in the study. A warm transparent compress applied to subject’s forehead served as a thermal impact. A custom-made IPPG system operating at green polarized light was used to monitor the subject’s face continuously and simultaneously with skin temperature and electrocardiogram (ECG) recordings. We found that the thermal impact leads to an increase in the amplitude of blood pulsations (BPA) simultaneously with the skin temperature increase. However, a multiple increase in BPA remained after the compress was removed, whereas the skin temperature returned to the baseline. Moreover, the BPA increase and duration of the vasomotor response was associated with the degree of external heating. Therefore, the IPPG method allows us to quantify the parameters of capillary blood flow during local thermal exposure to the skin. This proposed technique of assessing the thermal reactivity of microcirculation can be applied for both clinical use and for biomedical research. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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17 pages, 1654 KiB

Open AccessArticle

Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks

by Alicja Kwasniewska, Jacek Ruminski and Maciej Szankin

Appl. Sci. 2019, 9(20), 4405; https://doi.org/10.3390/app9204405 - 17 Oct 2019

Cited by 21 | Viewed by 4656

Abstract

Estimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies [...] Read more.

Estimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermal imagery by enhancing acquired sequences using Deep Neural Networks (DNN). To perform extensive benchmark evaluation, we acquired two thermal datasets using FLIR^® cameras with a spatial resolution of 80 × 60 and 320 × 240 from 71 volunteers in total. In-depth analysis of the proposed Convolutional-based Super Resolution model showed that for images downscaled with a factor of 2 and then super-resolved using Deep Learning (DL) can lead to better RR estimation accuracy than from original high-resolution sequences. In addition, if an estimator based on a dominating peak in the frequency domain is used, SR can outperform original data for a down-scale factor of 4 and images as small as 20 × 15 pixels. Our study also showed that RR estimation accuracy is better for super-resolved data than for images with color changes magnified using algorithms previously applied in the literature for enhancing vital signs patterns. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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21 pages, 1360 KiB

Open AccessArticle

3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

by Frédéric Bousefsaf, Alain Pruski and Choubeila Maaoui

Appl. Sci. 2019, 9(20), 4364; https://doi.org/10.3390/app9204364 - 16 Oct 2019

Cited by 84 | Viewed by 10473

Abstract

Remote pulse rate measurement from facial video has gained particular attention over the last few years. Research exhibits significant advancements and demonstrates that common video cameras correspond to reliable devices that can be employed to measure a large set of biomedical parameters without [...] Read more.

Remote pulse rate measurement from facial video has gained particular attention over the last few years. Research exhibits significant advancements and demonstrates that common video cameras correspond to reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject. A new framework for measuring and mapping pulse rate from video is presented in this pilot study. The method, which relies on convolutional 3D networks, is fully automatic and does not require any special image preprocessing. In addition, the network ensures concurrent mapping by producing a prediction for each local group of pixels. A particular training procedure that employs only synthetic data is proposed. Preliminary results demonstrate that this convolutional 3D network can effectively extract pulse rate from video without the need for any processing of frames. The trained model was compared with other state-of-the-art methods on public data. Results exhibit significant agreement between estimated and ground-truth measurements: the root mean square error computed from pulse rate values assessed with the convolutional 3D network is equal to 8.64 bpm, which is superior to 10 bpm for the other state-of-the-art methods. The robustness of the method to natural motion and increases in performance correspond to the two main avenues that will be considered in future works. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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11 pages, 1400 KiB

Open AccessArticle

Video-Based Contactless Heart-Rate Detection and Counting via Joint Blind Source Separation with Adaptive Noise Canceller

by Kanghyu Lee, Junmuk Lee, Changwoo Ha, Minseok Han and Hanseok Ko

Appl. Sci. 2019, 9(20), 4349; https://doi.org/10.3390/app9204349 - 15 Oct 2019

Cited by 10 | Viewed by 3155

Abstract

Driver assistance systems are a major focus of the automotive industry. Although technological functions that help drivers are improving, the monitoring of driver state functions receives less attention. In this respect, the human heart rate (HR) is one of the most important bio-signals, [...] Read more.

Driver assistance systems are a major focus of the automotive industry. Although technological functions that help drivers are improving, the monitoring of driver state functions receives less attention. In this respect, the human heart rate (HR) is one of the most important bio-signals, and it can be detected remotely using consumer-grade cameras. Based on this, a video-based driver state monitoring system using HR signals is proposed in this paper. In a practical automotive environment, monitoring the HR is very challenging due to changes in illumination, vibrations, and human motion. In order to overcome these problems, source separation strategies were employed using joint blind source separation, and feature combination was adopted to maximize HR variation. Noise-assisted data analysis was then adopted using ensemble empirical mode decomposition to extract the pure HR. Finally, power spectral density analysis was conducted in the frequency domain, and a post-processing smoothing filter was applied. The performance of the proposed approach was tested based on commonly employed metrics using the MAHNOB-HCI public dataset and compared with recently proposed competing methods. The experimental results proved that our method is robust for a variety of driving conditions based on testing using a driving dataset and static indoor environments. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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15 pages, 6045 KiB

Open AccessArticle

Physiological Driver Monitoring Using Capacitively Coupled and Radar Sensors

by Ivan D. Castro, Marco Mercuri, Aakash Patel, Robert Puers, Chris Van Hoof and Tom Torfs

Appl. Sci. 2019, 9(19), 3994; https://doi.org/10.3390/app9193994 - 24 Sep 2019

Cited by 23 | Viewed by 3381

Abstract

Unobtrusive monitoring of drivers’ physiological parameters is a topic gaining interest, potentially allowing to improve the performance of safety systems to prevent accidents, as well as to improve the driver’s experience or provide health-related services. In this article, two unobtrusive sensing techniques are [...] Read more.

Unobtrusive monitoring of drivers’ physiological parameters is a topic gaining interest, potentially allowing to improve the performance of safety systems to prevent accidents, as well as to improve the driver’s experience or provide health-related services. In this article, two unobtrusive sensing techniques are evaluated: capacitively coupled sensing of the electrocardiogram and respiration, and radar-based sensing of heartbeat and respiration. A challenge for use of these techniques in vehicles are the vibrations and other disturbances that occur in vehicles to which they are inherently more sensitive than contact-based sensors. In this work, optimized sensor architectures and signal processing techniques are proposed that significantly improve the robustness to artefacts. Experimental results, conducted under real driving conditions on public roads, demonstrate the feasibility of the proposed approach. R peak sensitivities and positive predictivities higher than 98% both in highway and city traffic, heart rate mean absolute error of 1.02 bpm resp. 2.06 bpm in highway and city traffic and individual beat R-R interval 95% percentile error within ±27.3 ms are demonstrated. The radar experimental results show that respiration can be measured while driving and heartbeat can be recovered from vibration noise using an accelerometer-based motion reduction algorithm. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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15 pages, 2194 KiB

Open AccessArticle

Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry

by Mark van Gastel, Wim Verkruysse and Gerard de Haan

Appl. Sci. 2019, 9(18), 3857; https://doi.org/10.3390/app9183857 - 13 Sep 2019

Cited by 17 | Viewed by 3714

Abstract

Pulse-oximetry has become a core monitoring modality in most fields of medicine. Typical dual-wavelength pulse-oximeters estimate blood oxygen saturation (SpO₂) levels from a relationship between the amplitudes of red and infrared photoplethysmographic (PPG) waveforms. When captured with a camera, the PPG [...] Read more.

Pulse-oximetry has become a core monitoring modality in most fields of medicine. Typical dual-wavelength pulse-oximeters estimate blood oxygen saturation (SpO₂) levels from a relationship between the amplitudes of red and infrared photoplethysmographic (PPG) waveforms. When captured with a camera, the PPG waveforms are much weaker and consequently the measurement is more sensitive to distortions and noises. Therefore, an indirect method has recently been proposed where, instead of extracting the relative amplitudes from the individual waveforms, the waveforms are linearly combined to construct a collection of pulse signals with different pulse signatures, each corresponding to a specific oxygen saturation level. This method has been shown to outperform the conventional ratio-of-ratios based methods, especially when adding a third wavelength. Adding wavelengths, however, complicates the calibration. Inaccuracies in the calibration model threaten the performance of the method. Opto-physiological models have been shown earlier to provide useful calibration parameter estimates. In this paper, we show that the accuracy can be improved using a data-driven approach. We performed 5-fold cross validation on recordings with variations in oxygen saturation and optimized for pulse quality. All evaluated wavelength combinations, also without visible red, meet the required ISO standard accuracy with the calibration from the proposed method. This scalable approach is not only helpful to fine-tune the calibration model, but even allows computation of the calibration model parameters from scratch without prior knowledge of the data acquisition details, i.e., the properties of camera and illumination. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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15 pages, 5216 KiB

Open AccessArticle

Unobtrusive Respiratory Flow Monitoring Using a Thermopile Array: A Feasibility Study

by Ilde Lorato, Tom Bakkes, Sander Stuijk, Mohammed Meftah and Gerard de Haan

Appl. Sci. 2019, 9(12), 2449; https://doi.org/10.3390/app9122449 - 15 Jun 2019

Cited by 13 | Viewed by 3463

Abstract

Low-resolution thermal cameras have already been used in the detection of respiratory flow. However, microbolometer technology has a high production cost compared to thermopile arrays. In this work, the feasibility of using a thermopile array to detect respiratory flow has been investigated in [...] Read more.

Low-resolution thermal cameras have already been used in the detection of respiratory flow. However, microbolometer technology has a high production cost compared to thermopile arrays. In this work, the feasibility of using a thermopile array to detect respiratory flow has been investigated in multiple settings. To prove the concept, we tested the detector on six healthy subjects. Our method automatically selects the region-of-interest by discriminating between sensor elements that output noise and flow-induced signals. The thermopile array yielded an average root mean squared error of

1.59 b r e a t h s p e r m i n u t e

. Parameters such as distance, breathing rate, orientation, and oral or nasal breathing resulted in being fundamental in the detection of respiratory flow. The paper provides the proof-of-concept that low-cost thermopile-arrays can be used to monitor respiratory flow in a lab setting and without the need for facial landmark detection. Further development could provide a more attractive alternative for the earlier bolometer-based proposals. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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Review

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32 pages, 3834 KiB

Open AccessReview

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

by Fatema-Tuz-Zohra Khanam, Ali Al-Naji and Javaan Chahl

Appl. Sci. 2019, 9(20), 4474; https://doi.org/10.3390/app9204474 - 22 Oct 2019

Cited by 62 | Viewed by 9821

Abstract

Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance [...] Read more.

Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the diversity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications. Full article

(This article belongs to the Special Issue Contactless Vital Signs Monitoring)

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