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Human cardiopulmonary recognition using close-range Doppler radar
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|Title:||Human cardiopulmonary recognition using close-range Doppler radar|
|Authors:||Kiriazi, John Elias|
|Keywords:||human cardiopulmonary recognition|
|Issue Date:||Dec 2010|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [December 2010]|
|Abstract:||The work in this dissertation demonstrates the effective use of continuous-wave Doppler radar measurements and analysis, to accurately assess a human subject's sleep position and corresponding cardiopulmonary activity. The approach involves a precise dual-frequency radar measurement with a quantitative analysis of the return signal, in terms of intensity and phase modulation magnitude. The first parameter is a measure of the radar cross section of the portion of the torso surface that is moving due to respiration and heartbeat activity. It is defined as the cardiopulmonary effective radar cross section.|
The second parameter corresponds to the maximum displacement of the torso surface in the direction of incidence. Contributions of this work to the art and science of electrical engineering include the design of experiments and analyses of Doppler radar cardiopulmonary measurements for a twenty-subject population; establishment of the definition for cardiopulmonary effective radar cross section along with measurement techniques and considerations; the first absolute measurements of the cardiopulmonary effective radar cross section at three different subject orientations; assessment of system accuracy in measuring radar cross section, displacement magnitude and motion rate; development of a model for human torso geometry and motion characteristics; and the first measurement of torso displacement for a considerably large population of subjects while preserving the low frequency content of the signal. Detailed statistical analyses were performed on the data of the twenty subjects to relate the measured effective radar cross section and torso displacement magnitude to sleeping position. Results from these statistics were the basis to develop a reliable decision algorithm to determine whether the subject is in a supine, prone, or side position. These findings significantly extend the function of human Doppler radar cardiopulmonary monitoring, to provide robust comprehensive physiological monitoring capabilities for unattended subjects.
|Description:||Ph.D. University of Hawaii at Manoa 2010.|
Includes bibliographical references.
|Appears in Collections:||Ph.D. - Electrical Engineering|
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