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아동댁내사고 예방을 위한 착용형 센서를 이용한 행동인지
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 조위덕 | - |
| dc.contributor.author | Park, Jung Wook | - |
| dc.date.accessioned | 2018-11-08T06:58:03Z | - |
| dc.date.available | 2018-11-08T06:58:03Z | - |
| dc.date.issued | 2010-02 | - |
| dc.identifier.other | 10691 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/4616 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :전자공학과,2010. 2 | - |
| dc.description.tableofcontents | Abstract ................................................................................................................9 Introduction........................................................................................................10 Related Work .....................................................................................................12 Pervasive Safety Management Systems....................................................................12 Multi-sensor Fusion...................................................................................................14 Analysis of Home Accidents..............................................................................16 Statistical Data Analysis............................................................................................16 Observation of Dangerous Activities in a Real Home Environment ........................17 Design of The Child Activity Recognition System ...........................................21 Wearable Sensor Device ...........................................................................................21 Detail Specification of Selected Sensors...................................................................24 Monitoring Application.............................................................................................25 Implementation..........................................................................................................25 Recognition Algorithm ......................................................................................27 Levels of Processing..................................................................................................28 Feature Extraction .....................................................................................................29 Distance Metrics........................................................................................................31 Distance-Based Clustering, Support Vector Machines .............................................32 Designing Event-driven Reactive Engine .................................................................33 Experiments in a Real Home Environment........................................................35 Conclusion and Future Work .............................................................................40 List of References ..............................................................................................42 Biographical Sketch ...........................................................................................45 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 아동댁내사고 예방을 위한 착용형 센서를 이용한 행동인지 | - |
| dc.title.alternative | Activity Recognition for Child Home Accident Prevention using Wearable Sensors | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Jung Wook Park | - |
| dc.contributor.department | 일반대학원 전자공학과 | - |
| dc.date.awarded | 2010. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 568537 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000010691 | - |
| dc.subject.keyword | 행동인지 | - |
| dc.subject.keyword | 유비쿼터스 | - |
| dc.subject.keyword | 착용형 센서 | - |
| dc.subject.keyword | 안전사고 예방 | - |
| dc.description.alternativeAbstract | In this thesis, we propose an activity recognition method using wearable sensors to prevent child home accidents. It is practically impossible to ask parents keep their eyes on babies 24 hours a day, 7 days a week. In order to prevent child home accident and reduce efforts of parents, new safety management method is required. Also early detection is needed to prevent unintentional injury at home. We analyze dangerous child activities, such as falls, poisoning, burns and electrocution, by utilizing statistical data sets and several observations. To recognize various dangerous activities, we attached only a single wearable device which includes a 3-axis accelerometer, an absolute pressure sensor, and a RFID reader to a child’s waist. The FFT analysis is adopted to extract features of the aggregated data, and SVMs are tested on these features. The overall accuracy of activity recognition using our proposed method was 95.42% with the SVMs. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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