A Real-Time, Portable Non-Invasive Monitoring System of Muscle Oxygen and pH in Trauma Patients

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$100,000.00
Award Year:
2008
Program:
STTR
Phase:
Phase I
Contract:
W81XWH-08-C-0111
Award Id:
85072
Agency Tracking Number:
A08A-037-0046
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1507 Plateau Lane, Rapid City, SD, 57703
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Bernt Askildsen
Proprietor
(605) 484-0055
bernt.askildsen@sdsmt.edu
Business Contact:
Janna Streleck
Business Manager
(605) 484-0680
jannastreleck@gmail.com
Research Institute:
SD SCHOOL OF MINES & TECHNOLOGY
Brian Hemmelman
501 East Saint Joseph Street
Rapid City, SD, 57703
(605) 394-1668
Nonprofit college or university
Abstract
War fighters face increased risk in the post 9-11 era of expanding terrorist activity and urban military conflicts. The problem is compounded by complex modern urban areas and modern rules of engagement often preclude the use of wide area offensive tactics. Consequently dismounted forces are called to enter challenging hostile terrain in search of select enemy targets. Many of these respondents are injured or killed each year due to improvised explosive devices detonated from remote locations. War fighters injured by these explosions often have significant loss of blood that leads to shock, which results in inadequate organ perfusion and tissue oxygenation. Hemorrhage is therefore a major cause of soldier death in the modern day battlefield. Resuscitation from shock aims to correct the mismatch between available oxygen and the demands of critical organs. Accurate knowledge of partial oxygen pressure, the oxygen saturation, and the pH of the peripheral muscle tissue support a successful resuscitation procedure. Therefore, this project will develop technology for a miniature device able to measure these parameters. The proposed system will utilize ultra bright directive light emitters with narrow photon energy spread in the 660nm to 1050nm spectrum. Each sensing objective will be optimized on a 3-layer phantom using an artificial neural network classifier scheme already proven to extract very reliable signals from highly cluttered ultra wide band raw data. The artificial neural network training strategy will take into account issues such as inhomogeneous medium, variation of light intensities, lack of monochromatic light sources, and the relation between light absorption, scatter and reflections. Under ideal conditions, the accuracy is expected exceed 95%.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government