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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成 | |
| dc.contributor.author | Kuo-Chung Hsu | en |
| dc.contributor.author | 徐國鐘 | zh_TW |
| dc.date.accessioned | 2021-06-15T05:46:28Z | - |
| dc.date.available | 2012-08-19 | |
| dc.date.copyright | 2010-08-19 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47060 | - |
| dc.description.abstract | 從環境中的資訊辨識出使用者當下的行為是挑戰的，為了提供使用者更合適的服務，了解使用者的行為將能更正確地提供使用者所需要的服務。在過去的研究裡，為了簡化問題的複雜度，往往有家中僅有單一居住者的假設。本文的目標是建立一個系統能學習多位居住者的行為模式，並能即時的辨識家中各個居住者的行為。
本文的貢獻主要有以下三點：第一，茲因在非侵入性環境中資訊的不充足，多人行為辨識的資料鏈結並不容易被分類。於是我們嘗試設計能夠推論資料鏈結的模型；第二，由於每個居住者的行為有可能被其他居住者影響，人與人之間的互動必須被納入考慮，這樣的動機使我們提出了一個多人行為辨識模型；第三，手動標記資料鏈結是一個十分耗費人力的工作，特別是在多人環境中，因此我們實作了一個自動標記方法。 | zh_TW |
| dc.description.abstract | Reliable recognition of activities from cluttered sensory data is challenging and important for a smart home to provide more desirable services. Traditionally, most of prior works often assume that there is always only one resident at home for the purpose of simplification of solving a complicated problem regarding multiple-resident activity recognition. Therefore, the goal of this thesis is to build a system which learns multiple-resident activity models to facilitate reliable activity recognition of each resident.
The main contribution of this thesis is three-fold. Firstly, due to the insufficiency of information in an environment using pervasive non-obtrusive sensors, data association for multiple-resident activity recognition is hard to be identified. Therefore, we aim to design a model which can infer the data association. Secondly, interactions among residents should be modeled since the activity of one resident may be influenced by another, and this concern motivates us to propose a multiple-resident activity recognition system which can adapt to the interaction between residents. Thirdly, because manually annotating data association is laborious, especially in an environment involving multiple residents, we design a mechanism which can automatically annotate the data association. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T05:46:28Z (GMT). No. of bitstreams: 1 ntu-99-R97922020-1.pdf: 2019474 bytes, checksum: 3564e67b33961b401c9031b5c083b4f8 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Table of Contents
口試委員會審定書 # 誌謝 i 中文摘要 iii Abstract iv Relevant Publications v Table of Contents vi List of Figures x List of Tables xii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Challenges 2 1.2.1 Challenges of Activity Recognition 3 1.2.2 Challenges of Multiple-resident Activity Recognition 3 1.3 Related Work 5 1.3.1 Multi-resident Dataset Collection 5 1.3.2 Activity Recognition 6 1.3.3 Multiple-Resident Activity Recognition 7 1.4 Objective 9 1.5 Thesis Organization 10 Chapter 2 Preliminaries 11 2.1 Mathematical Terminology 11 2.1.1 Metric Space 11 2.1.2 Probability Theory 12 2.2 Wireless Sensor Network (WSN) 13 2.3 Dynamic Bayesian Network (DBN) 17 2.3.1 Representation 18 2.3.2 Inference 19 2.3.3 Learning 20 2.4 Conditional Random Field (CRF) 22 2.4.1 Representation and Inference 23 2.4.2 Learning 24 Chapter 3 Activity Recognition System 25 3.1 Problem Definition 26 3.2 System Overview 26 3.3 Sensor Deployment 27 3.3.1 Data Association Annotation 30 3.4 Data Preprocessing 33 3.4.1 Quantization 35 3.4.2 Data Fusion 36 3.4.3 Feature Categorization 36 3.4.4 Feature Selection and Feature Vectors 37 3.5 Model Training and Activity Recognition 38 Chapter 4 Data Association Assisted Two-layer Multi-Resident Activity Recognition 39 4.1 System Overview 40 4.2 Comparison between DBN and CRF 41 4.3 First Layer - Data Association Model 42 4.3.1 Transition Table 43 4.4 Second Layer - Activity Model 44 4.4.1 Linear-Chain Conditional Random Fields (LCRF) 46 4.5 Preliminary Evaluation 47 4.5.1 CASAS Dataset 47 4.5.2 Data Association Evaluation 48 4.5.3 Correlation between Data Association and Activity Recognition 50 Chapter 5 Interaction Modeling Enhanced Multi-Resident Activity Recognition 52 5.1 Enhanced Second Layer 53 5.1.1 Parallel Hidden Markov Models (PHMMs) 53 5.1.2 Coupled Hidden Markov Models (CHMMs) 54 5.2 Interaction Feature Assisted Multiple-resident Activity Recognition 56 5.2.1 CHMM with interaction vertices 56 5.3 Preliminary Evaluation 60 5.3.1 Feature Vectors for this Evaluation 60 5.3.2 Experimental Result 63 Chapter 6 System Evaluation 65 6.1 Data Collection 65 6.1.1 Sensor Deployment 68 6.1.2 Middleware 70 6.1.3 Data Annotation 71 6.1.4 Data Collection Procedure 71 6.2 Experimental Result 72 6.2.1 Experiment: Two-layer Model 72 6.2.2 Experiment: Enhanced Second Layer 74 Chapter 7 Conclusion 76 7.1 Summary 76 7.2 Future Work 77 REFERENCE 78 | |
| dc.language.iso | en | |
| dc.subject | 動態貝氏網路 | zh_TW |
| dc.subject | 智慧家庭 | zh_TW |
| dc.subject | 行為辨識 | zh_TW |
| dc.subject | 多居住者 | zh_TW |
| dc.subject | 條件隨機場 | zh_TW |
| dc.subject | 資料鏈結 | zh_TW |
| dc.subject | Activity Recognition | en |
| dc.subject | Dynamic Bayesian Network (DBN) | en |
| dc.subject | Data Association | en |
| dc.subject | Conditional Random Field (CRF) | en |
| dc.subject | Multiple Residents | en |
| dc.subject | Smart Home | en |
| dc.title | 利用非侵入性環境感測器達成智慧型家庭下之多人行為辨識 | zh_TW |
| dc.title | Multiple-Resident Activity Recognition Using Pervasive Non-obtrusive Sensors in a Smart Home Environment | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蘇木春,郭斯彥,蔡超人,馮明惠 | |
| dc.subject.keyword | 智慧家庭,行為辨識,多居住者,條件隨機場,資料鏈結,動態貝氏網路, | zh_TW |
| dc.subject.keyword | Smart Home,Activity Recognition,Multiple Residents,Conditional Random Field (CRF),Data Association,Dynamic Bayesian Network (DBN), | en |
| dc.relation.page | 83 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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