Evaluating the robustness of a contact-less mHealth solution for personal and remote monitoring of blood oxygen saturation.

MHealth technologies play a fundamental role in epidemiological situations such as the ongoing outbreak of COVID-19 because they allow people to self-monitor their health status (e.g. vital parameters) at any time and place, without necessarily having to physically go to a medical clinic. Among vital parameters, special care should be given to monitor blood oxygen saturation (SpO2), whose abnormal values are a warning sign for potential COVID-19 infection. SpO2 is commonly measured through the pulse oximeter that requires skin contact and hence could be a potential way of spreading contagious infections. To overcome this problem, we have recently developed a contact-less mHealth solution that can measure blood oxygen saturation without any contact device but simply processing short facial videos acquired by any common mobile device equipped with a camera. Facial video frames are processed in real-time to extract the remote photoplethysmographic signal useful to estimate the SpO2 value. Such a solution promises to be an easy-to-use tool for both personal and remote monitoring of SpO2. However, the use of mobile devices in daily situations holds some challenges in comparison to the controlled laboratory scenarios. One main issue is the frequent change of perspective viewpoint due to head movements, which makes it more difficult to identify the face and measure SpO2. The focus of this work is to assess the robustness of our mHealth solution to head movements. To this aim, we carry out a pilot study on the benchmark PURE dataset that takes into account different head movements during the measurement. Experimental results show that the SpO2 values obtained by our solution are not only reliable, since they are comparable with those obtained with a pulse oximeter, but are also insensitive to head motion, thus allowing a natural interaction with the mobile acquisition device.

Using Contactless Facial Image Recognition Technology to Detect Blood Oxygen Saturation.

Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger life. As the situation continues to escalate, non-contact machines are used to assist patients at home to monitor their blood oxygen levels without encountering others. This paper uses a general network camera to capture the forehead area of a person's face, using the RPPG (remote photoplethysmography) principle. Then, image signal processing of red and blue light waves is carried out. By utilizing the principle of light reflection, the standard deviation and mean are calculated, and the blood oxygen saturation is computed. Finally, the effect of illuminance on the experimental values is discussed. The experimental results of this paper were compared with a blood oxygen meter certified by the Ministry of Health and Welfare in Taiwan, and the experimental results had only a maximum error of 2%, which is better than the 3% to 5% error rates in other studies The measurement time was only 30 s, which is better than the one minute reported using similar equipment in other studies. Therefore, this paper not only saves equipment expenses but also provides convenience and safety for those who need to monitor their blood oxygen levels at home. Future applications can combine the SpO2 detection software with camera-equipped devices such as smartphones and laptops. The public can detect SpO2 on their own mobile devices, providing a convenient and effective tool for personal health management.

An Intelligent Classification Diagnosis Based on Blood Oxygen Saturation Signals for Medical Data Security Including COVID-19 in Industry 5.0

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is gradually valued due to its high prevalence, high risk, and high mortality. Alternative to the polysomnography (PSG) diagnosis, the proposed method assesses the subject's degree of illness considering the supply chain and Industry 5.0 requirement efficiently and accurately. This article uses the blood oxygen saturation (SpO(2)) signal count of the number of apnea or hypoventilation events during the sleep of the subject, calculating the apnea-hypopnea index (AHI) and the subject's disease level. SpO(2) signals are used to extract 35-D features based on the time domain, including approximate entropy, central tendency measure, and Lempel-Ziv complexity to accelerate the diagnosis process in supply chains. The feature selection process is reduced from 35 to 7 dimensions that benefits to the implementation in the practical supply chains in Industry 5.0 by extracting the extracted features. This article applies Pearson correlation coefficient selection, based on minimum redundancy-maximum correlation algorithm selection, and a wrapper based on the backward search algorithm. The accuracy rate is 86.92%, and the specificity is 90.7% under the selected random forest classifier. A random forest classifier was used to calculate the AHI index, and a linear regression analysis was performed with the AHI index obtained from the PSG. The result reaches a 92% accuracy rate in assessing the prevalence of OSAHS, satisfying the industrial deployment.

An Insight into Wearable Devices for Smart Healthcare Technologies

Physical fitness is the prime priority of people these days as everyone wants to see himself as healthy. There are numbers of wearable devices available that help human to monitor their vital body signs through which one can get an average idea of their health. Advancements in the efficiency of healthcare systems have fueled the research and development of high-performance wearable devices. There is significant potential for portable healthcare systems to lower healthcare costs and provide continuous health monitoring of critical patients from remote locations. The most pressing need in this field is developing a safe, effective, and trustworthy medical device that can be used to reliably monitor vital signs from various human organs or the environment within or outside the body through flexible sensors. Still, the patient should be able to go about their normal day while sporting a wearable or implanted medical device. This article highlights the current scenario of wearable devices and sensors for healthcare applications. Specifically, it focuses on some widely used commercially available wearable devices for continuously gauging patient's vital parameters and discusses the major factors influencing the surge in the demand for medical devices. Furthermore, this paper addresses the challenges and countermeasures of wearable devices in smart healthcare technology. © 2023 IEEE.

Overestimation of Oxygen Saturation Measured by Pulse Oximetry in Hypoxemia. Part 1: Effect of Optical Pathlengths-Ratio Increase.

On average, arterial oxygen saturation measured by pulse oximetry (SpO2) is higher in hypoxemia than the true oxygen saturation measured invasively (SaO2), thereby increasing the risk of occult hypoxemia. In the current article, measurements of SpO2 on 17 cyanotic newborns were performed by means of a Nellcor pulse oximeter (POx), based on light with two wavelengths in the red and infrared regions (660 and 900 nm), and by means of a novel POx, based on two wavelengths in the infrared region (761 and 820 nm). The SpO2 readings from the two POxs showed higher values than the invasive SaO2 readings, and the disparity increased with decreasing SaO2. SpO2 measured using the two infrared wavelengths showed better correlation with SaO2 than SpO2 measured using the red and infrared wavelengths. After appropriate calibration, the standard deviation of the individual SpO2-SaO2 differences for the two-infrared POx was smaller (3.6%) than that for the red and infrared POx (6.5%, p < 0.05). The overestimation of SpO2 readings in hypoxemia was explained by the increase in hypoxemia of the optical pathlengths-ratio between the two wavelengths. The two-infrared POx can reduce the overestimation of SpO2 measurement in hypoxemia and the consequent risk of occult hypoxemia, owing to its smaller increase in pathlengths-ratio in hypoxemia.

IoT-based COVID-19 Realtime Monitoring and Alerting System in Brunei

A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, also known as COVID-19) is a major problem for many countries in the world. Brunei is also affected by this COVID-19 pandemic in many ways. To alleviate the burden on the health ministry, we developed a low-cost, reliable Internet of Things (IoT) based real-time health monitoring system to diagnose early COVID-19 symptoms for patients at home. This diagnosis includes the three important physiological parameters such as body temperature, heart rate and oxygen saturation level (SpO2) in the blood. This system comes with an OLED LCD to display the three parameters. Apart from that, these parameters are also displayed on a mobile dashboard using the Cayenne IoT platform for easy access. This system was evaluated against many people, and the results were compared against the industry-standard pulse oximeters which are remarkably close and dependable. © 2022 IEEE.

A Wireless Body Temperature and Oxygen Saturation Monitoring system based on Android Smartphones

Remote health monitoring is one topic that needs a lot of attention due to the rising number of pandemics. Body fever and low blood oxygen saturation level is clear symptom of COVID-19. Different data display and transmission systems can make monitoring vital indicators such as body temperature, pulse oximeter and heart rate. In this research, thermometry and pulse oximetry have been designed and built with an online monitoring system based on Android. The MAX30205 thermometer sensor was utilized in this study to detect body temperature with high precision. Also, the MAX30102 module was used to detect blood oxygen saturation and heart rate with proper accuracy. The sensors were controlled by an ESP32 microcontroller on the TTGO board, and the measured temperature, blood oxygen saturation level and heart rate were transmitted through Bluetooth to Android devices. These three parameters can be assessed long distances using these circuit and application designs in pandemic conditions. The device's performance was tested successfully and compared with the results of a reference thermometer and finger pulse oximeter. © 2022 IEEE.

Measurement of Blood Oxygen based on Remote-Photoplethysmography

SpO2, also known as blood oxygen saturation, is a vital physiological indicator in clinical care. Since the outbreak of COVID-19, silent hypoxia has been one of the most serious symptoms. This symptom makes the patient's SpO2 drop to an extremely low level without discomfort and causes medical care delay for many patients. Therefore, regularly checking our SpO2 has become a very important matter. Recent work has been looking for convenient and contact-free ways to measure SpO2 with cameras. However, most previous studies were not robust enough and didn't evaluate their algorithms on the data with a wide SpO2 range. In this paper, we proposed a novel non-contact method to measure SpO2 by using the weighted K-nearest neighbors (KNN) algorithm. Five features extracted from the RGB traces, POS, and CHROM signals were used in the KNN model. Two datasets using different ways to lower the SpO2 were constructed for evaluating the performance. The first one was collected through the breath-holding experiment, which induces more motion noise and confuses the actual blood oxygen features. The second dataset was collected at Song Syue Lodge, which locates at an elevation of 3150 meters and has lower oxygen concentration in the atmosphere making the SpO2 drop between the range of 80% to 90% without the need of holding breath. The proposed method outperforms the benchmark algorithms on the leave-one-subject-out and cross-dataset validation. © 2022 IEEE.

iMask: An IoT-based Intelligent Mask to Identify and Track COVID-19 Suspects

COVID-19 has become a global health concern, and wearing masks is a key measure to curb COVID-19 from rapidly spreading. While COVID-19 patients can be accurately determined using Rapid Antigen and PCR tests, these tests are costly, time-consuming, invasive, and uncomfortable. Further, they should be performed in a specialized environment despite showing the COVID-19 symptoms such as fever, cough, rapid heart rate, shortness of breath, and low blood oxygen saturation level. To this end, this study aims to automatically identify, and track the COVID-19 suspects in real-time by embedding smart sensors to face masks. The mask was developed to gather the data related to five major symptoms of COVID-19: body temperature, cough, heart rate, breathing pattern, and blood oxygen level. Data collected using smart sensors were used to identify and track COVID-19 suspects using Deep Neural Networks, the Internet of Things (IoT), and Artificial Intelligence (AI). Yielded results showed the proposed mask can identify COVID-19 suspects 92% accurately. © 2022 IEEE.

Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an effort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more difficult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19 patients were recruited from the Azizia primary health care sector/Wasit Health Directorate/Ministry of Health to form different combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths. © 2022 Ahmed M. Dinar et al.

Effectiveness of Plasmapheresis Treatment in the Treatment of Patients with COVID-19 Disease.

Background: According to the World Health Organization, COVID-19 management focuses primarily on infection prevention, case management, case monitoring, and supportive care. However, due to the lack of evidence, no specific anti-SARS-CoV-2 treatment is recommended. This study aimed to evaluate the effectiveness of plasmapheresis treatment in COVID-19 patients with symptoms of pulmonary involvement on the computed tomography (CT) of the lung. Methods: In 2021, an experimental study in critically ill patients admitted to the COVID-19 ward in the Hazrat-e Rasool hospital diagnosed with COVID-19 was conducted in the second phase (pilot study). The diagnosis was confirmed according to clinical signs, CT scan of the lung, and the Polymerase chain reaction (PCR) test. All patients received the usual treatments for COVID-19 disease and underwent plasmapheresis at a dose of 40 cc/kg daily up to 4 doses. All patients were observed for 24 hours for complications of plasmapheresis treatment and simultaneously for symptoms of COVID-19, after which only routine care measures were performed. The next day and 2 weeks after resumption of the treatment, patients experienced COVID-19 symptoms, including shortness of breath, cough, and fever. Blood oxygen saturation, and treatment results were evaluated. Qualitative and rank variables were described using absolute and relative frequencies and quantitative parametric variables were used using mean and confidence interval. Frequencies were compared in groups using the chi-square test. All tests were performed in 2 directions and P > 0.05 was considered statistically significant. Results: Of the 120 patients studied, 79 (65.8%) were men and 41 (34.2%) were women. The mean age was 60.30 ± 15.61 years (22-95 years). The mean hospital stay was 12.89 days ± 7.25 days (2-38 days). Increased blood oxygen saturation levels in patients had an increasing trend. Inflammatory indices had a downward trend in patients. The frequency of plasmapheresis had no significant effect on reducing the downward trend of inflammatory markers. The greatest reduction occurred in the first plasmapheresis. Conclusion: Finally, according to the findings, plasmapheresis is one of the appropriate treatments to improve patients' symptoms and reduce cytokine storm. Recovered patients had lower levels of inflammatory markers than those who died.

MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals.

This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user's face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.

The accuracy of pulse oximetry in measuring oxygen saturation by levels of skin pigmentation: a systematic review and meta-analysis.

BACKGROUND: During the COVID-19 pandemic, there have been concerns regarding potential bias in pulse oximetry measurements for people with high levels of skin pigmentation. We systematically reviewed the effects of skin pigmentation on the accuracy of oxygen saturation measurement by pulse oximetry (SpO2) compared with the gold standard SaO2 measured by CO-oximetry. METHODS: We searched Ovid MEDLINE, Ovid Embase, EBSCO CINAHL, ClinicalTrials.gov, and WHO International Clinical Trials Registry Platform (up to December 2021) for studies with SpO2-SaO2 comparisons and measuring the impact of skin pigmentation or ethnicity on pulse oximetry accuracy. We performed meta-analyses for mean bias (the primary outcome in this review) and its standard deviations (SDs) across studies included for each subgroup of skin pigmentation and ethnicity and used these pooled mean biases and SDs to calculate accuracy root-mean-square (Arms) and 95% limits of agreement. The review was registered with the Open Science Framework ( https://osf.io/gm7ty ). RESULTS: We included 32 studies (6505 participants): 15 measured skin pigmentation and 22 referred to ethnicity. Compared with standard SaO2 measurement, pulse oximetry probably overestimates oxygen saturation in people with the high level of skin pigmentation (pooled mean bias 1.11%; 95% confidence interval 0.29 to 1.93%) and people described as Black/African American (1.52%; 0.95 to 2.09%) (moderate- and low-certainty evidence). The bias of pulse oximetry measurements for people with other levels of skin pigmentation or those from other ethnic groups is either more uncertain or suggests no overestimation. Whilst the extent of mean bias is small or negligible for all subgroups evaluated, the associated imprecision is unacceptably large (pooled SDs > 1%). When the extent of measurement bias and precision is considered jointly, pulse oximetry measurements for all the subgroups appear acceptably accurate (with Arms < 4%). CONCLUSIONS: Pulse oximetry may overestimate oxygen saturation in people with high levels of skin pigmentation and people whose ethnicity is reported as Black/African American, compared with SaO2. The extent of overestimation may be small in hospital settings but unknown in community settings. REVIEW PROTOCOL REGISTRATION: https://osf.io/gm7ty.

Development of Low-Cost and Portable Pulse Oximeter Device with Improved Accuracy and Accessibility.

Purpose: In a clinical setting, blood oxygen saturation is one of the most important vital sign indicators. A pulse oximeter is a device that measures the blood oxygen saturation and pulse rate of patients with various disorders. However, due to ethical concerns, commercially available pulse oximeters are limited in terms of calibration on critically sick patients, resulting in a significant error rate for measurement in the critical oxygen saturation range. The device's accessibility in developing countries' healthcare settings is also limited due to portability, cost implications, and a lack of recognized need. The purpose of this study was to develop a reliable, low-cost, and portable pulse oximeter device with improved accuracy in the critical oxygen saturation range. Methods: The proposed device measures oxygen saturation and heart rate using the reflectance approach. The rechargeable battery and power supply from the smartphone were taken into account, and the calibration in critical oxygen saturation values was performed using Prosim 8 vital sign simulator, and by comparing with a standard pulse oximeter device over fifteen iterations. Results: The device's prototype was successfully developed and tested. Oxygen saturation and heart rate readings were both accurate to 97.74% and 97.37%, respectively, compared with the simulator, and an accuracy of 98.54% for the measurement of blood oxygen saturation was obtained compared with the standard device. Conclusion: The accuracy of oxygen measurement attained in this study is significant for measuring oxygen saturation for patients in critical care, anesthesia, pre-operative and post-operative surgery, and COVID-19 patients. The advancements made in this research have the potential to increase the accessibility of pulse oximeter in resource limited areas.

A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO(2) Monitoring Using Smartphone Cameras

Blood oxygen saturation (SpO(2)) is an important indicator forpulmonary 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 SpO(2). 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 SpO(2) 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 SpO(2) information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO(2) 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%.

Photoplethysmographic imaging of hemodynamics and two-dimensional oximetry

Background and Objectives: A review of recent papers devoted to actively developing methods of photoplethysmographic imaging (PPGI) of blood volume pulsations in vessels and non-contact two-dimensional oximetry on the surface of the human body is carried out. Results: The physical fundamentals and technical aspects of PPGI and oximetry have been considered. The diversity of physiological parameters available for analysis by PPGI has been shown. The prospects of PPGI technology have been discussed. The possibilities of non-contact determination of blood oxygen saturation SpO2 (saturation of pulse O2) have been described. The relevance of remote determination of the level of oxygenation due to the spread of a new coronavirus infection SARS-CoV-2 (COVID-19) has been emphasized. Most of the works under consideration cover the period of 2010–2021 years. © 2022 Editorial Office of Acta Horticulturae Sinica. All rights reserved.

Internet of Things based Wearable Smart Gadget for COVID-19 Patients Monitoring

It is extremely difficult to monitor and manage infected patients during the COVID-19 pandemic. This IoT wearable monitoring gadget is developed to measure the indicators of COVID-19. Patients' GPS data is used to notify medical authorities of their infection status. A wearable sensor is affixed to the body and connected to an edge node in the IoT cloud where the data is processed and analyzed in order to monitor health. A temperature sensor, GPS, SpO2 sensor, IR sensor, and accelerometer make up the system. The Arduino UNO processor is used in this gadget. The patient's body temperature is obtained using the temperature sensor. The location of the infected patient is tracked using a GPS sensor. Human movement is detected using an accelerometer. The SpO2 sensor measures the blood oxygen saturation level. The heart rate is detected using a pulse sensor. Information about preventive measures, warnings, and actions is stored in a cloud database. COVID-19 symptom readings are measured using this approach for monitoring and analysis. © 2022 IEEE.

Determination of Pulse Oximetric Parameters of the Body by Photoplethysmography

A sharp increase in respiratory tract morbidity, including from coronavirus infection, increases the need for individual means of continuous diagnosis. Currently, such devices as pulse oximeters are widely used. With their help, the heart rate and arterial blood oxygen saturation are monitored, which makes it possible to respond in a timely manner to the deterioration of the human body. The principle of operation of pulse oximeters is based on the method of photoplethysmography. The development of medical devices, including pulse oximeters, using modern technologies is an actual direction of radio electronics and allows to achieve high accuracy, efficiency, reduction of dimensions and ease of use of these devices. © 2022 IEEE.

The relationship between CT severity infections and oxygen saturation in patients infected with COVID-19, a cohort study.

Introduction: Thin-section chest computed tomography is an important diagnostic test and utilized to determine the severity of lung involvement in COVID-19 pneumonia. The goal of this study is to examine the relationship between CT severity and the oxygen saturation level of individuals with COVID-19. Method: This is a single-center retrospective study of COVID-19 patients that were admitted at a COVID-19 hospital. Patients confirming COVID-19 with PCR testing, patients undergoing lung CT-scan and measures of capillary oxygen saturation using pulse oximetry at the time of admission were all included. Result: The total number of the cases were 105. The age was classified into four age groups, with the majority of them falling into the fourth to sixth decade of life (42, 40%). Diabetes was the most common comorbidity disease (29, 27.6%). Pulse oximetry showed hypoxemia in 87 (82.9%) cases. The most common CT finding was ground glass opacities (GGO) (45, 42.9%). The data showed a significant positive correlation between oxygen saturation and CT severity in patients infected with covid-19. Conclusion: These findings support the importance of using pulse oximetry to monitor COVID-19 patients in order to evaluate or even estimate their clinical situations.

An Intelligent Classification Diagnosis based on Blood Oxygen Saturation Signals for Medical Data Supply Chain Including COVID-19 in Industry 5.0

Obstructive sleep apnea-hypopnea syndrome (OSAHS) has been gradually valued due to its high prevalence, high risk, and high mortality. This article is to find an alternative to the polysomnography (PSG) OSAHS diagnosis method and assesses the subject's degree of illness considering the supply chain and Industry 5.0 requirement, efficiently, accurately and easily. The blood oxygen saturation (SpO2) signal is used to count the number of apnea or hypoventilation events. It extracts 35-dimensional features based on the time domain to enhance the process resilience, including approximate entropy, Centralized Trend Measurement (CTM), and LZ complexity for the diagnosis process in supply chains. This article summarizes the Oxygen Desaturation Index (ODI) characteristics. The feature selection process is reduced from 35 to 7 dimensions and benefits the implementation in the practical supply chains in industry 5.0. A 92% accuracy rate is reached in assessing the prevalence of OSAHS, satisfying the industrial deployment. IEEE