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研究生:李濟宇
研究生(外文):Ji-yu Li
論文名稱:使用多層式決策法則探勘方法之個人行為辨識系統開發
論文名稱(外文):Development of Personalized Activity Recognition System Using Multi-level Decision Rule Mining Approach
指導教授:郭耀煌郭耀煌引用關係
指導教授(外文):Yao-huang Kuo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:72
中文關鍵詞:行為辨識資料探勘序列探測
外文關鍵詞:Activity RecognitionSequential DetectionData Mining
相關次數:
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近年來在情境感知系統逐漸發展之下,許多相關議題包括如何蒐集環境中資料以及如何轉換這些未經過處理的資料成為有用的情境感測資訊也備受關注。而這些未經過處理的資料可以經由邏輯的隱含推論或關聯法則的推理之下成為在情境感知系統中可用的情境感測資訊。在此篇論文中提出了多層式的模型以建構符合個人化行為辨識系統。首先透過無順序關係的關聯法則資料探勘方式從使用者的歷史活動資料庫中尋找使用者個人風格並建立出個人化的活動推論決策法則。接著再經由這些建立出來的活動推論決策法則辨識使用者進行長時間行為期間所包含的活動序列。藉由對活動序列探測分析與比對後將使用者的行為模式作歸納,並且從其中推理出使用者的個人行為習慣且建立出個人化行為推論決策法則。最後則是經由個人化行為推論決策法則來辨識並測試是否可以準確辨識出使用者所進行的行為。因此相較於現今許多情境感知系統中的決策法則皆是系統設計者或領域專家所建立,此系統是透過發掘使用者的行為與活動風格所建立的決策法則更能適用於每一位使用者。有別於一般的行為辨識系統,在此系統中所採納的情境感測資訊來源為各種環境中所佈置之裝置與感應器而不是由監視器與攝影機所拍攝錄製的影像畫面。而透過模擬實驗的分析,系統在20%雜訊的干擾下所建立出的個人化行為決策法則也能夠有83.33%的辨識準確率。
In recent years, development of context-aware system is focusing on how to collect raw data sensed from environment and combine these raw data to form some
useful information. According to logic implicit inferences and association rules between these raw data, high-level context information can be generated. In this thesis, a three-layer hierarchical system is proposed to generate personalization rules for action and activity recognition. Multi-level decision rule mining approach in our system not only discovers personal habit of device using, but also finds personal pattern of devices operation manner. First, this system processes association mining of data mining procedure, which mines from user’s database of historical action and constructs rules for recognizing actions. Second, it creates action sequence recognized by using action rules generated before. Sequential detection method is adopted to find out the most frequent sequential pattern which is seemed as personal activity habit of device operating manner. Finally, these non-sequential rules and sequential patterns are checked in order to recognize user’s personal activity. The three-layered hierarchical model generates rules according user’s habit rather than being specified by designer who only creates a general rule set. Therefore, it can build different rules which fit for different users, and provide personalized activity recognition rules. And the simulation results reveal that the activity recognition accuracy with noise rate 20% interference is 83.33%.
LIST OF TABLES .............................................................................................................................. XI
LIST OF FIGURES ........................................................................................................................... XII
1. CHAPTER 1 INTRODUCTION .................................................................................................... 1
1.1 MOTIVATION ..................................................................................................................................... 1
1.2 CONTRIBUTION .................................................................................................................................. 3
1.3 THESIS ORGANIZATION ......................................................................................................................... 5
2. CHAPTER 2 BACKGROUND AND RELATED WORK ..................................................................... 6
2.1 SURVEY OF ACTION AND ACTIVITY RECOGNITION ....................................................................................... 6
2.1.1 Actions and Activities ............................................................................................................. 6
2.1.2 Activity Recognition System ................................................................................................... 8
2.2 ASSOCIATION RULE MINING .................................................................................................................. 9
2.2.1 The Rule Model of Data Mining ............................................................................................. 9
2.2.2 The Rule Model of Association Mining ................................................................................. 11
2.3 SURVEY OF SEQUENTIAL RECOGNITION .................................................................................................. 14
2.3.1 Sequential Pattern Detection ............................................................................................... 14
2.3.2 Activity Recognition Systems based on Sequential Detection .............................................. 16
3. CHAPTER 3 HIERARCHICAL ACTIVITY RECOGNITION MODEL .................................................. 18
3.1 ELEMENTS OF CONDITION LAYER .......................................................................................................... 19
3.2 ELEMENTS OF ACTION LAYER ............................................................................................................... 26
3.3 ELEMENTS OF ACTIVITY LAYER ............................................................................................................. 30
4. CHAPTER 4 ADAPTIVE ACTIVITY RECOGNITION RULE MINING ................................................ 33
4.1 PROBLEM FORMULATION.................................................................................................................... 35
4.2. ACTION RULE MINING ...................................................................................................................... 38
4.2.1 Find all frequent Condition Sets ........................................................................................... 39
4.2.2 Search for significant Condition Sets .................................................................................... 44
4.3 ACTIVITY RULE MINING ...................................................................................................................... 47
4.3.1 Construct length table .......................................................................................................... 48
4.3.2 Backtrack common Actions .................................................................................................. 51
5. CHAPTER 5 SIMULATION ....................................................................................................... 54
5.1 SIMULATION ENVIRONMENT ............................................................................................................... 54
5.2 SIMULATION RESULTS ........................................................................................................................ 58
5.2.1 Simulation 1: SN ................................................................................................................... 59
X
5.2.2 Simulation 2: AN .................................................................................................................. 60
5.2.3 Simulation 3: PN ................................................................................................................... 61
6. CHAPTER 6 CONCLUSIONS AND FUTURE WORK ..................................................................... 66
6.1 CONCLUSIONS ................................................................................................................................. 66
6.2 FUTURE WORK ................................................................................................................................ 67
REFERENCES ................................................................................................................................. 68
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