scholarly journals A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO2 Monitoring Using Smartphone Cameras

2021 ◽  
Author(s):  
Xin Tian ◽  
Chau-Wai Wong ◽  
Sushant Ranadive ◽  
Min Wu
Keyword(s):  
Skin Irritation ◽  
Estimation Method ◽  
Absolute Error ◽  
Clinical Findings ◽  
Blood Oxygen ◽  
Blood Oxygen Level ◽  
Color Channel ◽  
Blood Oxygen Saturation ◽  
Important Indicator ◽  
Channel Ratio

Blood oxygen saturation (SpO2) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO2. Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO2 monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO2 information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO2 prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.

scholarly journals A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO2 Monitoring Using Smartphone Cameras

2021 ◽  
Author(s):  
Xin Tian ◽  
Chau-Wai Wong ◽  
Sushant Ranadive ◽  
Min Wu
Keyword(s):  
Skin Irritation ◽  
Estimation Method ◽  
Absolute Error ◽  
Clinical Findings ◽  
Blood Oxygen ◽  
Blood Oxygen Level ◽  
Color Channel ◽  
Blood Oxygen Saturation ◽  
Important Indicator ◽  
Channel Ratio

Blood oxygen saturation (SpO2) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO2. Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO2 monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO2 information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO2 prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.

scholarly journals Remote Blood Oxygen Estimation From Videos Using Neural Networks

2021 ◽  
Author(s):  
Joshua Mathew ◽  
Xin Tian ◽  
Min Wu ◽  
Chau-Wai Wong
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Clinical Findings ◽  
Network Architectures ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Estimation Scheme ◽  
Physiological Sensing ◽  
The Impact ◽  
Channel Combination

<div>Blood oxygen saturation (SpO<sub>2</sub>) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic. Clinical findings show that it is possible for COVID-19 patients to have significantly low SpO<sub>2</sub> before any obvious symptoms. The prevalence of cameras has motivated researchers to investigate methods for monitoring SpO<sub>2 </sub>using videos. Most prior schemes involving smartphones are contact-based: They require a fingertip to cover the phone's camera and the nearby light source to capture re-emitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO<sub>2</sub> estimation scheme using smartphone cameras. The scheme analyzes the videos of a participant's hand for physiological sensing, which is convenient and comfortable, and can protect their privacy and allow for keeping face masks on.</div><div>We design our neural network architectures inspired by the optophysiological models for SpO<sub>2</sub> measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO<sub>2</sub> measurement, showing the potential of our proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO<sub>2</sub> estimation performance.</div>

scholarly journals Dye-free retinal angiography using blood-oxygenation modulation

2018 ◽  
Author(s):  
Lewis E MacKenzie ◽  
Tushar R Choudhary ◽  
Javier Fernandez Ramos ◽  
Nigel Benjamin ◽  
Christian Delles ◽  
...  
Keyword(s):  
Blood Circulation ◽  
Multispectral Imaging ◽  
Skin Irritation ◽  
Blood Oxygenation ◽  
Fluorescence Angiography ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Non Invasive ◽  
Invasive Angiography ◽  
Angiography Sequences

Fluorescence angiography (FA) is widely used for studying and diagnosing abnormalities in the retinal blood circulation, but has associated risks of nausea, skin irritation, and even death. We describe a new non-invasive angiography technique: Blood Oxygenation Modulation Angiography, in which multispectral imaging of a transient perturbation in blood-oxygen saturation, yields angiography sequences similar to FA, including key features such as sequential filling of choroidal and retinal-vessels, which underpin assessment of circulation health. This is the first non-invasive angiography technique capable of visualizing these circulation features.

scholarly journals Remote Blood Oxygen Estimation From Videos Using Neural Networks

2021 ◽  
Author(s):  
Joshua Mathew ◽  
Xin Tian ◽  
Min Wu ◽  
Chau-Wai Wong
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Clinical Findings ◽  
Network Architectures ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Estimation Scheme ◽  
Physiological Sensing ◽  
The Impact ◽  
Channel Combination

<div>Blood oxygen saturation (SpO<sub>2</sub>) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic. Clinical findings show that it is possible for COVID-19 patients to have significantly low SpO<sub>2</sub> before any obvious symptoms. The prevalence of cameras has motivated researchers to investigate methods for monitoring SpO<sub>2 </sub>using videos. Most prior schemes involving smartphones are contact-based: They require a fingertip to cover the phone's camera and the nearby light source to capture re-emitted light from the illuminated tissue. In this paper, we propose the first convolutional neural network based noncontact SpO<sub>2</sub> estimation scheme using smartphone cameras. The scheme analyzes the videos of a participant's hand for physiological sensing, which is convenient and comfortable, and can protect their privacy and allow for keeping face masks on.</div><div>We design our neural network architectures inspired by the optophysiological models for SpO<sub>2</sub> measurement and demonstrate the explainability by visualizing the weights for channel combination. Our proposed models outperform the state-of-the-art model that is designed for contact-based SpO<sub>2</sub> measurement, showing the potential of our proposed method to contribute to public health. We also analyze the impact of skin type and the side of a hand on SpO<sub>2</sub> estimation performance.</div>

scholarly journals Link of Peripheral Blood Oxygen Level with Exercise

2019 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Oxygen Saturation ◽  
Human Blood ◽  
Peripheral Blood ◽  
Pulse Oximeter ◽  
Blood Oxygen ◽  
Oxygen Level ◽  
Blood Oxygen Level ◽  
Blood Oxygen Saturation

The objective of present study was used to analyze the relation of peripheral blood oxygen level with exercise. It is the measurement of oxygenated hemoglobin to the total of human blood. In this project there were 200 subjects participated who’s age between 18-22 studied in BZU. In this study we performed t.test. We used a device pulse oximeter to judge the blood oxygen saturation.

scholarly journals PSIX-9 Impacts of bunk management on feedlot performance, carcass characteristics, hydrogen sulfide concentration and blood oxygen saturation in steers fed 25 or 50% modified distillers grains plus solubles

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 422-422
Author(s):  
Rebecca L Moore ◽  
Cierrah J Kassetas ◽  
Leslie A LeKatz ◽  
Bryan W Neville
Keyword(s):  
Hydrogen Sulfide ◽  
Oxygen Saturation ◽  
Management Strategy ◽  
Distillers Grains ◽  
Feed Ratio ◽  
Sulfide Concentration ◽  
Blood Oxygen ◽  
Blood Oxygen Saturation ◽  
Feedlot Performance ◽  
Arterial Blood

Abstract One hundred and twenty-six yearling angus steers (initial body weight 445.87 ± 7.13 kg) were utilized in a 2 x 2 factorial design to evaluate the impacts of bunk management and modified distillers grains plus solubles (mDGS) inclusion on feedlot performance, hydrogen sulfide concentrations and blood oxygen saturation. Treatments included bunk management strategy either control bunk management (CON; clean bunks at the time of next day’s feeding) or long bunk management (LONG; feed remaining at time of next day’s feeding), and two inclusion rates of mDGS either 25% or 50% (DM Basis). On d 0, 7, 14, 21, 28 and 35 rumen gas samples were collected via rumenocentesis, and arterial blood samples were collected on two steers from each pen. No differences (P ≥ 0.09) were observed for dry matter intake, average daily gain and gain-to-feed ratio for bunk management or mDGS inclusion. Hot carcass weight, ribeye area, marbling score and quality grade were not affected (P ≥ 0.48) by either bunk management or mDGS inclusion. Back fat was greater (P = 0.04) for CON steers compared to LONG (1.30 vs 1.12 ± 0.05cm, respectively), but was not affected (P = 0.59) by mDGS inclusion. Steers on CON had greater (P = 0.03) yield grades compared to LONG (3.21 vs 2.96 ± 0.11, respectively). Bunk management strategy did not impact hydrogen sulfide concentrations or blood oxygen saturation (P = 0.82). Hydrogen sulfide concentrations increased (P &lt; 0.001) with increasing mDGS inclusion. Blood oxygen saturation was influenced by day of sampling (P = 0.01). Blood oxygen saturation was not affected (P = 0.07) by mDGS inclusion. The fact that ruminal hydrogen sulfide concentrations increased while blood oxygen saturation remained similar raises questions about the quantity of hydrogen sulfide and metabolic fate of excess hydrogen sulfide in the blood of ruminant animals.

scholarly journals Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2862
Author(s):  
Yakun Xie ◽  
Dejun Feng ◽  
Sifan Xiong ◽  
Jun Zhu ◽  
Yangge Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Estimation Method ◽  
Absolute Error ◽  
Large Error ◽  
Estimation Methods ◽  
Height Estimation ◽  
Building Height ◽  
Remote Sensing Imagery ◽  
High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m-3.76 m, which can achieve high-precision estimation of building height.

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