SmartPhOx: Smartphone-Based Pulse Oximetry Using a Meta-Region Of Interest

Demlabim, Firmin Leroy Kateu; Jakllari, Gentian; Chaput, Emmanuel

doi:10.1109/percom53586.2022.9762399

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…Thus, authors in 34 provide add-on hardware to measure SpO2 whose results meet the food and drug authority (FDA) accuracy standard when tested on six subjects only. Another smartphone-based pulse oximeter solution based on meta region of convergence (MROI), the ratio of ratio (RoR), and linear regression method are presented in 35 . This method though that does not require any additional hardware has the limitation that it cannot estimate low SpO2 values.…”

Section: Video Ppg-based Vitals Estimationmentioning

confidence: 99%

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

Mehmood,

Sarouji,

Rahman

et al. 2023

Preprint

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In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learnmore about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasivetools for rapid and continuous monitoring of body vitals that reflect the status of one’s overall health. In this backdrop, this workproposes a deep learning approach to turn a smartphone—the popular hand-held personal gadget—into a diagnostic tool tomeasure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), andrespiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG)of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip byplacing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filteredand/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks(CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise,the contribution of this paper is two-fold: 1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data usingcustom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); 2) anovel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to asingle-lead ECG signal. The significance of this work is two-fold: i) it allows rapid self-testing of body vitals (e.g., self-monitoringfor covid19 symptoms), ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrialfibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases,and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead toreduction in health insurance costs.

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Section: Video Ppg-based Vitals Estimationmentioning

confidence: 99%

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

Mehmood,

Sarouji,

Rahman

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Sensors may be put on the earlobe or the toe in addition to the fingertip. In the ubiquitous monitoring environment, the sensor unit’s signals interface with a miniature processing unit, which then transmits the data to the healthcare unit through wireless methods such as WLAN, and so on [ 105 ].…”

Section: Sensorsmentioning

confidence: 99%

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

et al. 2022

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In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

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“…Smartphone-based solutions for monitoring blood oxygen saturation have been explored previously, employing various solutions used to gather and stabilize the PPG signal 1 , augment the IR-filtered broad-band camera sensor 2 , and filter the resultant signal for noise or outlier correction 3 . Some solutions require extra hardware, such as a color filter or external light source 1,2,[4][5][6] , whereas others rely only on the in-built smartphone hardware and employ software techniques to process the PPG signal 3,[7][8][9][10][11] . These prior works indicate that there is potential for smartphone-based SpO 2 monitors to fill gaps in access to care, but lack validation data on a full range of clinically relevant SpO 2 levels.…”

Section: Introductionmentioning

confidence: 99%

Smartphone camera oximetry in an induced hypoxemia study

et al. 2022

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Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO2) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO2 sensing system using a varied fraction of inspired oxygen (FiO2) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO2 values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO2 while identifying positive cases of low SpO2 < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work.

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SmartPhOx: Smartphone-Based Pulse Oximetry Using a Meta-Region Of Interest

Cited by 5 publications

References 27 publications

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Smartphone camera oximetry in an induced hypoxemia study

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