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研究生:吳宗衡
研究生(外文):Wu, Tsung-Heng
論文名稱:以慣性量測元件為基礎之上下樓層人體行為辨識系統
論文名稱(外文):An IMU-Based Human Behavior Recognition System for Upstairs and Downstairs Movement
指導教授:曾煜棋曾煜棋引用關係
指導教授(外文):Tseng, Yu-Chee
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊科學與工程研究所
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:2010
畢業學年度:99
語文別:英文
論文頁數:37
中文關鍵詞:慣性量測元件人體行為辨識高度偵測室內定位定位系統肢體感測網路無線感測網路特徵比對最長共同子序列最長共同子字串
外文關鍵詞:IMUhuman behavior recognitionaltitude detectionindoor localizationpositioningbody sensor networkwireless sensor networkpattern-matchinglongest common subsequencelongest common substring
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近年來,以無線射頻技術為基礎的室內定位系統在精準度上有不少的提升。然而,飄移問題是造成定位誤差的一項重要因素。在多樓層定位中的飄移問題將造成不可接受的重大誤差。因此,在本篇論文中,我們提出了一個以慣性量測元件為基礎之上下樓層人體行為辨識系統。我們提出了數個創新的演算法,藉由比對目前特徵與訓練特徵來辨識人體行為。利用我們的系統,多層樓定位系統可以在沒有改變樓層的人體行為發生時,將定位結果鎖定在相同的樓層,藉此輔助定位結果的修正。如此一來,定位系統中樓層間的飄移問題將得以解決。
The accuracy of RF-based indoor localization has been improved in recent years. However, drifting problem has been an important factor of causing localization inaccuracy. In multi-story indoor localization, drifting problem between floors is not acceptable. In this thesis, we propose a system to detect human behavior of changing floors, including elevator going up/down floors and going upstairs/downstairs using Inertial Measurement Unit (IMU) sensor. We proposed novel algorithms to recognize human behaviors by matching the current pattern with training patterns. By using our scheme, multi-story indoor localization systems are able to fix the positioning results in the same floor when no floor changing behaviors are detected. Therefore we are able to solve the drifting problem of changing floors.
Contents
Abstract i
Acknowledgements iii
Table of Contents iv
List of Figures vi
1 Introduction 1
2 RelatedWorks 3
3 Problem Definition 5
4 Observation 6
4.1 Choosing Meaningful Measurement: Acceleration Observation . . . . . . . . . 6
4.2 Choosing Meaningful Measurement: Euler Angles Observation . . . . . . . . . 8
4.3 Feature of Elevator Measurement . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.4 Feature of Stairs Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.5 Observation of Different Persons and Different Body Parts . . . . . . . . . . . 11
5 Methodology 15
5.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5.2 Hardware: IMU Sensor Measurement . . . . . . . . . . . . . . . . . . . . . . 17
5.3 Software I: Training Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5.3.1 Data Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5.3.2 Training Pattern Formation I: Pattern Segmentation . . . . . . . . . . . 18
5.3.3 Training Pattern Formation II: Pattern Acquisition . . . . . . . . . . . 19
5.4 Software II:Online Phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.4.1 Data Filtering & Current Pattern Formation . . . . . . . . . . . . . . . 23
5.4.2 Behavior Detection I: Pattern Matching . . . . . . . . . . . . . . . . . 23
5.4.3 Behavior Detection II: Longest Common Substring . . . . . . . . . . . 26
5.4.4 Behavior Detection III: Longest Common Subsequence . . . . . . . . . 26
5.4.5 Detection Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6 Implementation and Experimentation 28
6.1 Design Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
6.1.1 LPF Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
6.1.2 Segmentation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.1.3 Trigger Boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.1.4 Discrete Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.2.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.2.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.3 Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.3.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . . . . 30
6.3.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
7 Conclusion 35
Bibliography 36
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