Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/100736

Detection of respiratory information using electromagnetic biosensors

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Item Summary

Title: Detection of respiratory information using electromagnetic biosensors
Authors: Padasdao, Bryson Eldon
Keywords: respiratory activity monitoring
physiological signals
Issue Date: Dec 2013
Publisher: [Honolulu] : [University of Hawaii at Manoa], [December 2013]
Abstract: Continuous respiratory activity monitoring can help predict and identify respiratory failure, and could potentially be life-saving. Recent wearable, wireless technology allows users to monitor important physiological signals, such as heart and respiratory rate, with the advantages of comfort and portability. However continuous, remote, monitoring requires regular battery replacement, which poses an inconvenience for the user, and a barrier to compliance and wide adoption of this technology. If respiratory energy is harvested, the energy can provide power for such a wearable biosensor, thus eliminating the need for regular battery replacement. The work in this dissertation demonstrates the feasibility of the zero-net energy biosensor concept. Contributions of this dissertation work to electrical engineering include the design of an electromagnetic respiratory effort harvester, the first human study data of respiratory rate and tidal volume detection using electromagnetic biosensors, and an investigation of simultaneous harvesting and sensing feasibility with a low-power system-on-chip. Respiratory effort is harvested through electromagnetic generation, while concurrently sensing critical respiratory parameters. Methodology for extracting respiratory parameters from electromagnetic generator outputs is investigated. High sensing accuracy of both respiratory rate and tidal volume is initially demonstrated using a mechanical target simulating respiratory motion. Close agreement between electromagnetic sensor outputs and a gold standard for respiratory measurements is further demonstrated through human testing.
Description: Ph.D. University of Hawaii at Manoa 2013.
Includes bibliographical references.
URI/DOI: http://hdl.handle.net/10125/100736
Appears in Collections:Ph.D. - Electrical Engineering



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