RAPID: Preventing the Spread of Coronavirus with Efficient Deep Learning
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$125,000Funder
National Science Foundation (NSF)Principal Investigator
Song HanResearch Location
United States of AmericaLead Research Institution
Massachusetts Institute of TechnologyResearch Priority Alignment
N/A
Research Category
Infection prevention and control
Research Subcategory
Barriers, PPE, environmental, animal and vector control measures
Special Interest Tags
Digital Health
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
The novel coronavirus, COVID-19 is a pandemic infecting people in the United States and around the world. It is of utmost importance to prevent the fast spread of the virus. This project will use artificial intelligence (AI) methods to slow down the infection by encouraging proper wear of Personal Protective Equipment (PPE) by hospital staff and by supporting social distancing. The planned method will help monitor dangerous activities pointed out by Center for Disease Control (CDC), such as hand-to-face contact, touching inside or crossing arms when taking off the gown and masks and social distancing. It will advance the national health, protect the healthcare workers and help the whole nation combat the pandemic.
Video understanding and activity recognition have made great progress in recent years. This project will apply artificial intelligence on a mobile platform for efficient activity recognition techniques to guide people's activities in healthcare settings, including patients', health care workers' and community residents'. To protect privacy in transmission to the cloud, the project applies the team's work on model compression techniques and neural architecture search to make the AI more compact and efficient so that it can be deployed on edge devices. As a result, videos can be locally processed; only key information or the detection result is sent over the cloud, preserving people's privacy. Finally, the project will efficiently deploy such algorithms on mobile devices and make it freely available in the hospitals.
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.
Video understanding and activity recognition have made great progress in recent years. This project will apply artificial intelligence on a mobile platform for efficient activity recognition techniques to guide people's activities in healthcare settings, including patients', health care workers' and community residents'. To protect privacy in transmission to the cloud, the project applies the team's work on model compression techniques and neural architecture search to make the AI more compact and efficient so that it can be deployed on edge devices. As a result, videos can be locally processed; only key information or the detection result is sent over the cloud, preserving people's privacy. Finally, the project will efficiently deploy such algorithms on mobile devices and make it freely available in the hospitals.
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.