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Unconstrained Apnea and Asthma Symptom Detection Using Ultra Wide Band Radar
UWB 레이더를 이용한 무구속적 무호흡 및 천식 증상 검출

DC Field Value Language
dc.contributor.advisor박광석-
dc.contributor.author고명준-
dc.date.accessioned2017-07-14T02:24:11Z-
dc.date.available2017-07-14T02:24:11Z-
dc.date.issued2017-02-
dc.identifier.other000000141216-
dc.identifier.urihttps://hdl.handle.net/10371/122467-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 바이오엔지니어링전공, 2017. 2. 박광석.-
dc.description.abstractAbstract

Unconstrained Apnea and Asthma Symptom Detection Using Ultra Wide Band Radar

MyungJun Koh

Interdisciplinary Program of Bioengineering
The Graduate School
Seoul National University

Respiration and coughing are evident vital signs to check ones condition are sound. Long-term respiration monitoring is needed to find out if the person is in good health condition. Long-term respiration monitoring can be cumbersome when devices are constraining subjects body. Measuring respirational signal with the unconstrained method is good for patients or babies or elders who have a lower level of enduring cumbersomeness.
The purpose of the study is to prove the feasibility of Ultra Wide Band (UWB) radar for detecting abnormal respiration. UWB Radar uses 3.1 to 10.6 GHz frequency range and transmit pulse with very short duration. Impulse radiation is 50 degrees from Vivaldi antenna. Its small size and low power consumption make UWB radar application for home or inside a vehicle. Apnea and coughing data was classified from normal breathing data with classifier such as Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) with statistic and frequency domain features.
For apnea detection, reference device data showed the best result with RF classifier with 100% mean sensitivity, 70% mean precision, 0.82 F measure and UWB radar data showed the best result with RF classifier with 100% mean sensitivity, 70% mean precision, 0.82 F measure.
For coughing detection, reference device data showed the best result with RF classifier with 100% mean sensitivity, 71% mean precision, 0.83 F measure while UWB radar data showed the best result with RF classifier with 100% mean sensitivity, 83% mean precision, 0.91 F-measure.
Detecting apnea and coughing while a person is moving or in different posture should be studied in future studies. This study expects more application of UWB radar in long-term vital sign monitoring.

Keyword: Ultra wide band radar, Respiration, Apnea, Cough, Asthma, Unconstrained
Student Number: 2015-21207
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dc.description.tableofcontentsChapter 1. Introduction 1
1.1 Background 1
1.1.1 Breathing 3
1.1.2 Apnea 4
1.1.3 Coughing 5
1.1.4 Asthma 6
1.2 Existing Methods 7
1.3 Ultra Wide Band Radar 9
1.4 Classifiers 15
1.4.1 Support Vector Machine 16
1.4.2 Decision Tree 18
1.4.3 Random Forest 19
1.5 Purpose 19
Chapter 2. Methods 20
2.1 Ultra Wide Band radar measurement 20
2.2 Experimental Protocol 21
2.3 Validation of Respiration Detection 24
2.4 Design of Classification 25
2.4.1 Feature Extracting 27
Chapter 3. Results 50
3.1 Feature Evaluation 50
3.2 Classification Result 50
3.3 Discussion 55
Chapter 4. Conclusion 56
References 57
Abstract in Korean 61
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dc.formatapplication/pdf-
dc.format.extent2026266 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectUWB radar-
dc.subjectApnea-
dc.subjectAsthma-
dc.subjectUnconstrained-
dc.subjectRespiration-
dc.subjectCough-
dc.subject.ddc660-
dc.titleUnconstrained Apnea and Asthma Symptom Detection Using Ultra Wide Band Radar-
dc.title.alternativeUWB 레이더를 이용한 무구속적 무호흡 및 천식 증상 검출-
dc.typeThesis-
dc.description.degreeMaster-
dc.citation.pages62-
dc.contributor.affiliation공과대학 협동과정 바이오엔지니어링전공-
dc.date.awarded2017-02-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Program in Bioengineering (협동과정-바이오엔지니어링전공)Theses (Master's Degree_협동과정-바이오엔지니어링전공)
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