SBIR Phase I: Particle Filtering Technology for Wearable Medical Sensors

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 0839734
Agency Tracking Number: 0839734
Amount: $99,932.00
Phase: Phase I
Program: SBIR
Awards Year: 2009
Solicitation Year: N/A
Solicitation Topic Code: BC
Solicitation Number: NSF 08-548
Small Business Information
Streamline Automation, LLC
1109 Chesterfield Road, Huntsville, AL, 35803
DUNS: 124289294
HUBZone Owned: Y
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Alton Reich
 MS
 (256) 694-5063
 alton.reich@streamlineautomation.biz
Business Contact
 Alton Reich
Title: MS
Phone: (256) 694-5063
Email: alton.reich@streamlineautomation.biz
Research Institution
N/A
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.

* information listed above is at the time of submission.

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