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SBIR Phase I: Particle Filtering Technology for Wearable Medical Sensors

Award Information

Agency:
National Science Foundation
Branch:
N/A
Award ID:
90968
Program Year/Program:
2009 / SBIR
Agency Tracking Number:
0839734
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Streamline Automation, LLC
3100 Fresh Way SW Huntsville, AL 35805-6720
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2009
Title: SBIR Phase I: Particle Filtering Technology for Wearable Medical Sensors
Agency: NSF
Contract: 0839734
Award Amount: $99,932.00
 

Abstract:

This Small Business Innovation Research Phase I project is aimed at developing improved noise filters for wearable medical instrumentation. Recently, medical sensing instrumentation for the monitoring of physiological signals has become increasingly wearable and noninvasive. However, because these sensors are now portable they will be exposed to higher levels of noise and artifacts (especially motion artifacts) than in controlled clinical scenarios. As a result, these sensors cannot perform reliably unless data is post-processed by a filter. Conventional filters (adaptive-recursive, wavelet, and others) are limited by their generic applicability and do not have the necessary performance. To address this, Streamline Automation, LLC (SA) and Worcester Polytechnic Institute (WPI) will develop particle filters (PF) based on physiological models. PF have been shown to outperform all other known filtering methods, especially for nonlinear systems, such as human physiology, with non-stationary and non-Gaussian noise (motion artifacts). However, so far PF have not been used in medical or biological applications. To make this possible, we propose a state-space modeling approach based on anatomical and physiological concepts. In Phase I, we will develop and demonstrate the particle filtering approach based on a cardiovascular-respiratory system state-space model to process wearable pulse oximeter signals. Potential applications are vast because particle filters have the potential to deliver robustness and reliability to any physiological monitoring hardware but have not yet been applied to biosignals. The focus of this project is increasing the robustness of the wearable pulse oximeter hardware such that it becomes useful in ambulatory monitoring. This technology should be applicable in urban and natural disaster areas where multiple traumatic injury victims must be triaged and evacuated (earthquakes, car accidents, explosions, tornadoes, etc). Other applications include monitoring of long-distance flight pilots, physical exercise monitoring, surgery and anesthesia, sleep apnea, patients with chronic cardiovascular or respiratory conditions, and remote monitoring under austere environments such as high altitude rescue teams, firefighters, and deep sea diving. Since particle filtering technology is not limited to pulse oximetry, a host of other applications exist, provided suitable mathematical models for the system and measurement are developed. Examples of these are the detection of faint fetal electrocardiogram, kidney dialysis monitoring, non-invasive glucose monitoring, and electromyogram filtering.

Principal Investigator:

Alton J. Reich
MS
2566945063
alton.reich@streamlineautomation.biz

Business Contact:

Alton J. Reich
MS
2566945063
alton.reich@streamlineautomation.biz
Small Business Information at Submission:

Streamline Automation, LLC
1109 Chesterfield Road Huntsville, AL 35803

EIN/Tax ID: 223885097
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No