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研究生： 卜帝亞
Aditya Fitri Hananta Putra
論文名稱： 使用一個智慧型手錶檢測駕駛員的手位置改善駕駛安全
Detecting Driver’s Hands Position using One Smartwatch to Improve Driving Safety
指導教授： 梁德容
Deron Liang
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 47
中文關鍵詞： distracted driving detection 、driver’s hand position 、smartwatch 、empirical mode decomposition 、Hilbert-Huang transform
外文關鍵詞： distracted driving detection, driver’s hand position, smartwatch, empirical mode decomposition, Hilbert-Huang transform
相關次數： 點閱：22  下載：0 
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    • 分心駕駛已經成為車禍的主要原因之一。它涉及不正確操作方向盤的動作。 我們提出了一種僅使用一個智能手錶來檢測駕駛員雙手位置的新方法。 我們使用 empirical mode decomposition（EMD）和Hilbert-Huang transform（HHT）從手錶的加速度計信號中提取重要的振動信息。 我們的實驗結果表明，使用一個智能手錶可以檢測到兩隻手的位置，兩隻手檢測精度值高達80％，單一檢測精度值高達96％。

    The distracted driving has become one of the major cause of car accidents. It is suggested that it involves the action of not handling the steering wheel properly. We propose a novel approach to detect the two hands position of the driver using only one smartwatch. We extract important vibration information from the watch’s accelerometer signal using empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT). Our experiment result shows that it is possible to detect two hands position using one smartwatch with accuracy value up to 80% for two hands detection, and up to 96% for single one detection.

    摘要 iii ABSTRACT v ACKNOWLEDGEMENT vi CONTENTS vii LIST OF FIGURES x LIST OF TABLES xi 1 Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Research Objective 3 1.4 Thesis Structure 4 2 Literature Review 5 2.1 Driver Safety 5 2.1.1 Distracted Driving Detection 5 2.1.2 Hand Position Detection 5 2.2 Smartwatch 6 2.2.1 Smartwatch for activity detection 7 2.2.2 Smartwatch uses for driving-related activity detection 7 2.3 Vibration analysis 8 2.3.1 Fast Fourier Transform 9 2.3.2 Hilbert-Huang Transform 9 2.4 Principal Component Analysis 9 2.5 Support Vector Machine 10 3 Methodology 11 3.1 System Architecture 11 3.2 Data Collection 12 3.3 Data Preprocessing 15 3.3.1 Partitioning 15 3.4 Feature Extraction 15 3.4.1 Raw Features 16 3.4.2 Improved Features using PCA 19 3.5 SVM Training and Testing 20 4 Experiments and Results 21 4.1 Signal Analysis 21 4.2 Features Analysis 21 4.3 Experiment Methods 23 4.3.1 Preliminary Experiment: Comparing Stationary Car Data with Real Moving Car 23 4.3.2 Classification using Different Signal Types 23 4.3.3 Evaluation of System’s Robustness – HHT Features 25 4.3.4 Evaluation of System’s Robustness – HHT + PCA Features 27 4.4 Discussions 29 5 Conclusions and Future Works 31 5.1 Conclusion 31 5.2 Future Works 31 References 33

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