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| Name | Class |
|---|---|
| eMedicalSentry | UNKNOWN |
| Applied Physics Laboratory | UNKNOWN |
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The purpose of this study is to determine whether vital signs can be extracted from video. A secondary purpose is to create a database, including raw video, of "ground truth" physiological data on human subjects in order to test current and/or future approaches developed to extract vital signs from video. This research may have an immediate impact on not only the assessment of risk for COVID-19 but also may provide a significant technological enhancement to Johns Hopkins Medicine's telemedicine capabilities.
The purpose of this study is to determine whether vital signs can be extracted from video. A secondary purpose is to create a database, including raw video, of "ground truth" physiological data on human subjects in order to test current and/or future approaches developed to extract vital signs from video. This research may have an immediate impact on not only the assessment of risk for COVID-19 but also may provide a significant technological enhancement to JHM's telemedicine capabilities. Participants may be asked to:
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| Measure | Description | Time Frame |
|---|---|---|
| Quantification of SpO2 (percentage) from video | The principal outcome is the capability to calculate a reliable and clinically useful measure of blood oxygenation from video. Our objective is to quantify SpO2 from video within +/- 0.5% from clinical standard. | 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| Clinical quality data obtained via contact sensors | Develop a comprehensive dataset of ground truth physiological signals along with multiple standoff sensors against which to test current/future physiological extraction algorithms. The contact sensors will include blood pressure (BP), pulse oximetry (SpO2), photoplethysmography (PPG), respiration rate (RR), and electrocardiogram (ECG). The standoff sensors will collect still and moving images using an infrared (IR) sensor, hyperspectral imager (HSI), and multiple red-green-blue (RGB) cameras. The dataset will be subjected to various advanced statistical analyses, to include machine learning approaches, to determine the accuracy (both in value and waveform) of algorithmic approaches to remote physiological measures. The long-term goal is to extract clinically useful physiological signals from video in order to provide a physician greater insight to the physiological functioning and/or psychophysiological state of the patient. |
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Inclusion Criteria:
Exclusion Criteria:
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Convenience
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| Name | Affiliation | Role |
|---|---|---|
| Edward Chen, MD | Johns Hopkins University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Johns Hopkins Asthma & Allergy Center | Baltimore | Maryland | 21224 | United States |
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| 3 years |