Wearable Diagnostics for Detection of COVID-19 Infection

This program is focused on the development a wearable diagnostic capability for the pre-/very early-symptomatic detection of COVID-19 infection. The pandemic COVID-19, a disease caused by a novel coronavirus, continues to spread worldwide. There is a dire and urgent need for development of rapid, accurate wearable diagnostics to identify and isolate pre-symptomatic COVID-19 cases and track/prevent the spread of the virus. Some of the ideal capabilities or specifications of a wearable diagnostic for COVID-19 infection are:

  • Platform must be designed for pre-detection (e.g., physiological monitoring of readiness) and early detection of infection and pathogenic response that can be utilized pre-clinically leading to use at point-of-need role of care 1/2, local doctor’s office, emergency departments, urgent care centers and immediate care clinics.
  • The capability should be “wearable”, non- or minimally-invasive and be able to assess physiological markers to monitor the health state of the user. A single device is preferred, but a combination of technologies is acceptable.
  • Device(s) should be designed to be worn for continuous physiological monitoring in a non-obtrusive manner and should not affect the daily activity of the wearer. Sampling of physiological markers/antibodies/biomarkers can occur “on demand” to conserve power. Device should be worn until exposure has been verified or until a medical professional has deemed the device is no longer needed.
  • Results should be easy to interpret by non-laboratory personnel and results should be collected and able to be saved and shared in a standard and secure format.
  • The device must be able to be stored and operated between 4°C to 45°C.
  • Physiologic surveillance for COVID-19 positive individuals that do not yet show clear medical symptoms is an ultimate goal. Physiological signatures therefore must produce predictive algorithms that can be tied into validated and relevant antibody/molecular measurements.
  • Must have an established manufacturing capability for the platform and assay kits on a large-scale.

The deliverable at the end of the project is to obtain Emergency Use Authorization for the new wearable capability and be ready to distribute the device and test kits. The research project award recipients were selected from the Offerors who responded to MTEC’s Request for Project Proposals (20-12-COVID-19_Diagnostics).

Modified PPD Test for Detection and Scalable Monitoring of the Covid-19 Antibody and Beyond

Project Team: Diomics Corporation

Award Amount: $1.43M (additional cost share = $204K)

Project Duration: 11 months

Project Objective: The team proposes to create a modern version of the Reactive Purified Protein Derivative (PPD) Tuberculosis test, the DIOTEST PPD C19, for over the counter, personal, real-time monitoring and detection of circulating antibodies to COVID-19. They propose to covalently link recombinant COVID-19 protein antigens to their unique polymer, DiomatTM, to create a novel diagnostic biopolymer. When applied to the patient, the product enables continuous monitoring and identification of positive individuals for long term physiologic surveillance. This testing platform can also be applied to future pandemic contagions by applying the same principles and technology. The overall objective is to develop a scalable, cost effective, easy to use, self-applied and clinician assisted monitoring capability for rapidly detecting hot spots of infection by expanding the capture of infection data by way of localized skin immune reactions. 

Wearable Physiologic Monitor with Real-time Continuous Data Acquisition and Analysis for Early Detection of COVID 19 Infection

Project Team: Sempulse Corporation

Award Amount: $1.84M

Project Duration: 15 months

Project Objective: To provide early detection of COVID 19 infection using wearable physiologic monitors with real-time continuous data acquisition, combined with dynamically emerging “best evidence” rule-based screening logic and machine learning-enabled predictive analytics. The Sempulse Physiologic Monitoring Platform (PMP), originally designed for remote monitoring of multiple warfighters and early detection of hemorrhagic decompensation, remotely collects outpatient biometric data during the latency period post-exposure to COVID 19. This data is analyzed with CTA’s VFusion, an Expert System guided machine learning-enabled clinical decision support early warning platform, which operationalizes expert systems, including a COVID-19 knowledge base of clinical observations, care pathways, interventions, and protocols, to provide early identification of COVID-19 infection in asymptomatic or pre-symptomatic patients, with high sensitivity (few missed cases) and specificity (few false positives).

Leveraging Wearable Ring Data for Early SARS CoV-2 Detection

Project Team: University of California, San Francisco

Award Amount: $5.53M

Project Duration: 32 months

Project Objective: The team proposes to use the Oura Ring, a commercially available device that can measure physiological metrics when worn on any finger. The project has three aims. Aim 1 is to perform SARS CoV-2 antibody testing with a high-accuracy assay to complete SARS CoV-2 case detection in the first TemPredict Study participants. This will provide ground truth infection status data for use in development of algorithms for early SARS CoV-2 detection, including identification of persons with asymptomatic infection. Aim 2 is to develop algorithms, based on physiological signals from Oura ring data (temperature, heart rate, heart rate variability, respiratory rate, and activity), to identify individuals with possible early SARS CoV-2 disease, and determine sensitivity and specificity of different cut-offs. Aim 3 is to develop protocols for, and conduct initial deployment and testing of, the Aim 2 algorithm in the second TemPredict Study’s participants. The researchers will deploy the algorithm to participants wearing Oura Rings to trigger (a) early SARS CoV-2 PCR testing (and metagenomic sequencing to detect other possible pathogens) and (b) early isolation in civilian populations. The project has the potential to improve early detection of COVID-19. This holds implications for pandemic control efforts, reopening the economy safely, and protecting the readiness of civilian and military populations. These aims will also strengthen infrastructure for addressing other infectious disease outbreaks. 

ICU-Grade Wearable Sensors with Novel Respiratory Biomarkers to Diagnose and Detect Pre- and Very Early Symptomatic COVID-19 Infection Using Predictive Machine Learning Algorithms

Project Team: Sibel Health

Award Amount: $2.40 (additional cost share = $1.02M)

Project Duration: 10 months

Project Objective: The overall objective of the program is to obtain FDA EUA for the ANNETM One system as a wearable diagnostic system for pre and early symptomatic detection of COVID-19 infection. The core novelty of this system is centered on advanced soft, flexible sensors pioneered by our team over the past decade of cutting edge research. Beyond COVID-19, ANNETM One has clear implications in military medicine as a monitoring platform for combat casualties or assessing Warfighter status in austere environments.

Philips North America, LLC’s response to Wearable Diagnostic for Detection of COVID-19 Infection

Project Team: Philips Healthcare

Award Amount: $2.75M

Project Duration: 20 months

Project Objective: Leveraging this technology platform, a post-market, single-center observational study has been designed to collect physiologic vital signs and biometric data in asymptomatic or early-onset symptomatic individuals who are at high risk for contracting COVID-19. The primary objective of this study is to use the nonobtrusive, easy-to-use BioSticker System to continuously monitor individuals from pre-exposure through symptomatic onset of an active infection and subsequent confirmatory diagnosis of COVID-19 to validate biosensor analytics for early detection of COVID-19.

Early Covid-19 Detection Using Wearables

Project Team: Fitbit

Award Amount: $2.49 (additional cost share = $2.54M)

Project Duration: 14 months

Project Objective: The objectives of this project are to (1) develop and validate algorithms, informed by data collected from Fitbit devices and software, that can identify early signals of COVID-19, and (2) successfully commercialize those algorithms on Fitbit devices and software. The proposed scope of the effort for this effort is focused only on the development of COVID-19 early detection algorithms, not the wearable devices themselves. Self-reported COVID-19 and influenza test results will be collected from Fitbit users. These results will be used to train algorithms for detection of COVID-19 based on biometric data from Fitbit devices. A clinical trial for Software as a Medical Device (SaMD) submission to FDA will be designed and implemented to prospectively validate the sensitivity and specificity of the early detection algorithms developed. The validated algorithms will be deployed at scale with Fitbit devices and software, including user-facing experiences that present algorithm results to users and provide appropriate guidance on next steps.

FALCON – First ALert for COVID-19 ONset

Project Team: Empatica

Award Amount: $2.48M (additional cost share = $1.09M)

Project Duration: 14 months

Project Objective: Empatica plans to finalize the validation of its COVID-19 early detection model on its E4 wearable device, which has sensors that continuously transmit physiological data processed by proprietary algorithms to an app and Cloud-based monitoring platform. This validation effort will occur with a US hospital on at-risk patients. Since little is known as to how the virus will continue to spread, an early detection platform of COVID-19 contagion could prove essential in the coming years to help keep people safely back at work and active in their lives.