RAPID: In-Home Automated and Non-Invasive Evaluation of COVID-19 Infection with Commodity Smartphones

  • Funded by National Science Foundation (NSF)
  • Total publications:0 publications

Grant number: 2029520

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $200,000
  • Funder

    National Science Foundation (NSF)
  • Principal Investigator

    Wei Gao
  • Research Location

    United States of America
  • Lead Research Institution

    University of Pittsburgh
  • Research Priority Alignment

    N/A
  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Supportive care, processes of care and management

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Computer and Information Science and Engineering - A key to combat the Coronavirus Disease (COVID-19) pandemic is to prevent the pandemic from overloading the public healthcare system, so that sufficient medical resources could be available for hospitalized patients. This project will develop new mobile sensing and Artificial Intelligence (AI) techniques for in-home evaluation of COVID-19 infection in order to pursue automated and non-invasive screening of potential viral disease carriers. It aims to timely identify negative cases caused by other diseases with similar symptoms, and hence avoids unnecessary hospital visits as many as possible.

The proposed techniques will use commodity smartphones to measure the changes of humans? airway mechanics, which are uniquely correlated to COVID-19 infection. These measurements build on acoustic sensing with smartphones? built-in speakers and microphones. First, new acoustic waveforms will be designed to minimize acoustic signal distortion in human airways. Second, new signal processing techniques will be developed for accurate measurements. Third, deep learning techniques will be used to develop generic models that depict the core characteristics of airway mechanics. These techniques will be evaluated by lab testing and experiments with student volunteers. This research will enable identifying false positives of COVID-19 infection out of the clinic and could contribute to the containment of the virus spread and damage. The proposed technologies will be applicable to a wide variety of commodity smartphones and could also be used in daily practice with handmade mouthpieces. Broader impacts will also result from a variety of education and outreach activities. New courses will be developed to incorporate the outcome of this research, and the research outcome will be disseminated through technology transfer to industry. The outcome of this project, including source codes and collected data from student volunteers, will be maintained at the project repository (http://www.pitt.edu/~weigao/research_COVID19.html) for at least five years, and will be made available to the public community.

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.