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Smartphone Application for Mask Sizing and Projecting Quantitative Fit

Award Information
Agency: Department of Defense
Branch: Office for Chemical and Biological Defense
Contract: W911SR-17-C-0060
Agency Tracking Number: C2-0460
Amount: $1,049,965.50
Phase: Phase II
Program: SBIR
Solicitation Topic Code: CBD161-005
Solicitation Number: 2016.1
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-09-27
Award End Date (Contract End Date): 2019-09-26
Small Business Information
701 McMillian Way NW
Huntsville, AL 35806
United States
DUNS: 185169620
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dr. Kay Sun
 Senior Research Scientist
 (256) 726-4800
Business Contact
 Deborah Phipps
Phone: (256) 726-4884
Research Institution

Typical procedures for respirator masks sizing and protective fit test are time-consuming and user intensive, requiring taking anthropometricmeasurements of the wearer, sampling the wearers breathing zone and the use of specialized tools and equipment. The proposed softwareapp aims to quickly, accurately and automatically perform respirator mask sizing and predict protective fit using only 2D images captured onsmartphone cameras, thereby replacing the lengthy physical testing procedures and can even be performed in the field when needed. InPhase I, personalized 3D facial surfaces are generated through shape reconstruction in real-time from captured 2D images. Common facialanthropometric measurements are automatically extracted from the mesh and are then used to predict respirator mask size according toNational Institute for Occupational Safety and Health (NIOSH respirator test fit panels. Preliminary validation showed 5.1% facialanthropometric feature prediction error. Phase II will involve the development of individualized respiration models to simulate the movementof air and particulates through the mask and into the lungs in order to predict quantitative fit factor. Further validation studies will also beperformed to ensure the accuracy of the app predictions.

* Information listed above is at the time of submission. *

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