This project will develop, refine, and evaluate a smartphone-based solution to reliably track changes in blood oxygen saturation (SpO2) and respiration rate and volume. Recent analysis of COVID-19 patients have shown some unusual findings. For example, there is a discordance between the respiratory symptoms and the blood oxygen saturation levels. This can lead to sharp deterioration of patient status without the individual experiencing the usual signs of distress. Existing smartphone solutions do not work in detecting significant drop in blood oxygenation, which is essential to detect whether the person needs to be hospitalized. Accurate in-home tracking of respiratory signals and blood oxygenation levels can help to monitor and follow patients with COVID-19 and identify those who are stable vs. those who are deteriorating.

This project will enable two informative, scalable, and cost-effective measurements using smartphones: (i) SpO2 and (ii) respiration rate and volume changes. Although there are many standalone pulse oximeters on the market which are FDA approved and work well (accuracy of ±2%), most people don't have them and are unlikely to buy special purpose devices. Recently, several smartphone and smartwatch based apps have been released that claim to measure oxygen saturation, but they are not reliable. These applications simply use the phone's camera to measure the change in reflection. While these apps can capture pulse reliably, and even capture the blood hemoglobin concentration to some degree, it does not work for oxygen saturation as there are no separate signals to compare oxygenated against deoxygenated hemoglobin. In general, pulse oximetry works by measuring the light absorption in hemoglobin (transdermally) at two different wavelengths (red: 660nm and near-infrared: 940nm). Both of these bands can be found in broadband white LEDs, such as those used for flash on smartphones and can be read by the image sensors (cameras), as they use infrared for distance measurements in photographs. With optical filters attached to the smartphone flash, these two distinct bands can be separated out from the broadband source, captured by the phone's camera, and be used as a pulse oximeter. For monitoring respiration signal/rate, the project will build on the investigators' previous work on opioid overdose detection, which leverages the speakers and microphones of a smartphone to monitor the chest motion of a person in a contactless fashion. At a high level, the smartphone transmits inaudible high-frequency custom sound signals using the device's speaker. These signals are reflected by the subject's chest and recorded using the device's microphones. The chest motion due to breathing causes a change in these reflections as seen by the microphones. These changes can be detected and the respiration signal can be obtained using signal processing algorithms on the smartphone. This system can now be improved to detect changes in respiration rates caused due to the onset of viral infections and difficult breathing conditions like hypoxia.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Start
Project End
Budget Start
2020-06-15
Budget End
2020-11-30
Support Year
Fiscal Year
2020
Total Cost
$99,975
Indirect Cost
Name
Cornell University
Department
Type
DUNS #
City
Ithaca
State
NY
Country
United States
Zip Code
14850