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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Ting-Ying Li | en |
| dc.contributor.author | 李梃穎 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:46:16Z | - |
| dc.date.available | 2021-02-23 | |
| dc.date.copyright | 2018-02-23 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-01-29 | |
| dc.identifier.citation | [1] 'The Global Impact of Dementia: An analysis of prevalence, incidence, cost and trends,' in 'World Alzheimer Report 2015,' Alzheimer’s Disease International, 2015.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70150 | - |
| dc.description.abstract | 由於全球人口老化的現象逐步加劇，越來越多的年長者會罹患失智症，失智症會進一步影響到長者們的生活作息、健康狀況等等。以目前醫師在診斷一位失智症長輩的流程來說，如果想要知道最近幾個月內該位長輩在行為上是否有失智症的傾向，通常都需要詢問長者身邊的家人或照護者，如果這位長者長期獨居的話，那麼醫生在診斷上就會比較不方便、花時間。為了協助醫師的診斷，我們提出一套快速觀測失智症的輔助系統，長者只需要花2到4個小時的時間在一個智慧環境中執行一些工具性日常生活活動能力量表（IADL）的活動即可，而長者執行活動時透過動作感測器所產生的移動路徑將會根據失智症之室內遊走特性被萃取出來及分析，最後在利用機器學習演算法基於這些萃取出來的特徵來輔助判斷長者在行為上是否有失智症的傾向。這套輔助系統的輸出結果會將長者分成兩類，分別是失智症與非失智症，而使用第一個公開資料庫去分類7位失智症長者與225位非失智症長者時，精確率和召回率都高達98.3%，且ROC曲線下的面積為0.851；使用第二個我們自己收集的資料庫去分類9位失智症長者與21位非失智症長者時，精確率和召回率分別是89.9%和90.0%，且ROC曲線下的面積為0.921。 | zh_TW |
| dc.description.abstract | Because of the worldwide aging population, more and more elders suffer from dementia. Nowadays, it is inconvenient and time-consuming for doctors to diagnose whether elders who live independently have dementia because lots of diagnostic questions on a checklist must be asked first, and part of them even require a long-term observation. In order to help doctors and make this diagnostic process easier, we proposed a supporting system that can quickly monitor the elders and estimate the likelihood of them having dementia based on a behavioral test in 2 to 4 hours. During the behavioral test, the elders only need to perform some activities selected from so-called Instrumental Activities of Daily Living (IADL) in a smart home environment, and their movement trajectories will be extracted and analyzed from motion sensors deployed in the smart home environment according to their indoor wandering patterns. A machine learning algorithm is selected to carry out the classification based on our proposed features. Our system supports the classification of two classes, Dementia and Non-Dementia, and its average precision and recall for classifying 232 elders including 7 with dementia in first dataset are both up to 98.3% with the value of Area Under the ROC Curve (AUC-ROC) being 0.851, and those for classifying 30 elders including 9 with dementia in second dataset are 89.9% and 90.0% with the value of AUC-ROC being 0.921. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:46:16Z (GMT). No. of bitstreams: 1 ntu-107-R04944054-1.pdf: 3089223 bytes, checksum: fa958d170d62512790e0f0f8d70aebca (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Challenges 3 1.2.1 Classifying Elders as Dementia or Non-dementia after Short Amount of Monitoring Time 3 1.2.2 Extracting Useful Features only with Ambient Sensors 4 1.2.3 Overcoming Elders’ Mobility Problems 5 1.3 Related work 5 1.4 Objective 9 1.4.1 Detecting System only with Motion Sensors 9 1.4.2 Feature Extraction according to Indoor Wandering Pattern 10 1.4.3 Relationship between Elders’ Behaviors of Assigned Activities and Dementia 11 1.5 Thesis organization 11 Chapter 2 Preliminaries 13 2.1 Wandering and Repetitive Movements 13 2.2 Classification Algorithm 16 Chapter 3 Supporting System for Detecting Dementia 22 3.1 System Overview 22 3.1.1 Model Training 23 3.1.2 Classification after 25 3.2 Indoor Wandering Patterns 25 3.2.1 Related Work of Wandering Detection 26 3.2.2 Wandering Detection with K-repeating Substrings 29 3.3 Feature Extraction 39 Chapter 4 System Evaluation 42 4.1 Experimental Environment 42 4.1.1 CASAS Dataset 42 4.1.2 ZS Dataset 44 4.2 Evaluation of Training Model 48 4.2.1 Evaluation with CASAS Dataset 50 4.2.2 Evaluation with ZS Dataset 57 4.3 Discuss the Relationship between Elders’ Behaviors of Assigned Activities and Dementia 60 Chapter 5 Conclusion 62 5.1 Summary 62 5.2 Future Work 63 REFERENCE 65 | |
| dc.language.iso | en | |
| dc.title | 基於智慧家庭中的動作感測器與室內遊走特性所開發的失智症之非穿戴式偵測系統 | zh_TW |
| dc.title | A Non-wearable Dementia Detecting System based on Indoor Wandering Patterns Using PIR Motion Sensors in Smart Home | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳錫中,陳佳慧,陳淑惠,廖峻鋒 | |
| dc.subject.keyword | 失智症,快速觀測,智慧環境,動作感測器,室內遊走特性,機器學習, | zh_TW |
| dc.subject.keyword | Dementia,quickly monitor,smart home,motion sensors,indoor wandering pattern,machine learning, | en |
| dc.relation.page | 70 | |
| dc.identifier.doi | 10.6342/NTU201800238 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-01-30 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| ntu-107-1.pdf 目前未授權公開取用 | 3.02 MB | Adobe PDF |
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