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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳中明 | |
| dc.contributor.author | I-Hsin Chuang | en |
| dc.contributor.author | 莊宜欣 | zh_TW |
| dc.date.accessioned | 2021-07-11T14:36:36Z | - |
| dc.date.available | 2022-08-31 | |
| dc.date.copyright | 2017-08-31 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-15 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77881 | - |
| dc.description.abstract | 全球目前正面臨人口快速老化的問題。根據臺灣衛福部統計，2013至2015年65歲以上老年人口所有意外事故死亡事件中，跌倒為第二大主要死因；又根據美國疾病控制與預防中心統計，2012至2013年，65歲以上老年人口所有意外事故死亡事件中，55%為跌倒所導致，而在2000至2013年，65歲以上老年人口每十萬人中，因跌倒而死亡之比率將近成長兩倍。由此可見，跌倒成為高齡化社會之醫療照顧課題中急需解決之問題。
本研究提出同時使用氣壓傳感器與加速度傳感器之跌倒偵測方法，進入跌倒偵測方法前，使用氣壓傳感器之資訊，進行是否有一定程度之高度變化之判斷，若有，則進入跌倒偵測流程，高度變化作為Trigger Key以降低跌倒偵測所進行之較複雜運算。跌倒偵測流程分為兩階段，第一階段使用氣壓傳感器之資訊，並使用門檻值法進行是否進入第二階段之判斷，第二階段使用加速度傳感器之資訊，使用leave one-subject out進行訓練樣本與驗證樣本之分類，再使用支持向量機法進行是否為跌倒事件之判斷。 本實驗設計共有兩種情境：第一種情境為靜態之情況下執行動作之模擬，共24位受試者，執行日常生活動作與跌倒動作共5種；第二種情境為動態情況下執行動作之模擬，共9位受試者，執行日常生活動作與跌倒動作共15種。經過本研究所提出跌倒偵測方法之分類結果，第一種情境之敏感度、特異度與正確率分別為0.92、1、0.95；第二種情境之15種動作之敏感度、特異度與正確率分別為0.87、0.95、0.9，排除第15個跌倒動作，僅計算14種動作之敏感度、特異度與正確率分別為0.99、0.95、0.97。 | zh_TW |
| dc.description.abstract | Aging society is a global issue and growth of elderly population is growing faster. According to Ministry of Health and Welfare of Taiwan, fall events are the second cause of accident casualty of the elderly (people aging 65 years or older) during 2013 to 2015. In addition, statistics by Centers for Disease Control and Prevention of the U.S. also shows that 55% of accident casualty of the elderly caused by fall events during 2012 to 2013. Moreover, mortality of the elderly due to fall events grows nearly twice during 2000 to 2013. Therefore, fall events become an important issue among aging health care service.
In this study, we proposed an integrating fall detection using barometer and accelerometer. Our algorithm contains three stages: Trigger Key: using barometer and simple threshold method to detect whether there is a significant negative height change, Stage One: using barometer and simple threshold method to determine whether there is a possible fall event, Stage Two: using accelerometer and support vector machine method to classify whether it is a fall event. For experiment design, we have two different scenarios. The first scenario is static and including two daily activities and three fall events. The second scenario is non-static and including seven daily activities and eight fall events. Sensitivity, specificity and accuracy of static scenario are 92%, 100%, 95% and 99%, 95%, 97% for non-static scenario. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T14:36:36Z (GMT). No. of bitstreams: 1 ntu-106-R01548036-1.pdf: 1923132 bytes, checksum: 072f9e445963fe33dc443087eb49c550 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員委員審定書…………………………………………………………………#
誌謝 i 中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 ix 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 2 第二章 文獻探討 9 2.1 基於穿戴式裝置之跌倒偵測方法之主要演算法 9 2.2 基於穿戴式裝置之跌倒偵測方法之配戴位置 9 2.3 僅使用加速度傳感器之跌倒偵測研究 10 2.4 使用加速度傳感器與氣壓傳感器之跌倒偵測研究 12 第三章 實驗設計與研究方法 14 3.1 實驗設計 14 3.1.1 實驗器材 14 3.1.2 實驗對象 15 3.1.3 實驗流程 16 3.2 資料分析方法 20 3.2.1. 資料前處理 20 3.2.2. 特徵值擷取 21 3.2.3. 資料訓練與分類 27 第四章 結果與討論 30 4.1 第一種情境 31 4.1.1 Trigger Key 31 4.1.2 第一階段 32 4.1.3 第二階段 34 4.2 第二種情境 36 4.2.1 Trigger Key 36 4.2.2 第一階段 41 4.2.3 第二階段 44 第五章 結論與未來展望 48 參考文獻 49 | |
| dc.language.iso | zh-TW | |
| dc.title | 結合氣壓傳感器與加速度傳感器之跌倒偵測方法 | zh_TW |
| dc.title | Integrating Fall Detection Using Barometer and Accelerometer | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李佳燕,莊競程 | |
| dc.subject.keyword | 跌倒偵測,氣壓傳感器,加速度傳感器,門檻值法,支持向量機法, | zh_TW |
| dc.subject.keyword | fall detection,barometer,accelerometer,simple threshold method,support vector machine method, | en |
| dc.relation.page | 53 | |
| dc.identifier.doi | 10.6342/NTU201703528 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-16 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| 顯示於系所單位： | 醫學工程學研究所 | |
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