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研究生： 潘思言
Setyan Pamungkas
論文名稱： 以穿戴單一智慧型手錶利用多種建模策略偵測操縱方向盤之手部位置
On Several Modeling Approach for Detecting Steering Handling Position Using One Smartwatch
指導教授： 梁德容 博士
Professor Deron Liang
張欽圳 博士
Professor Chin-Chun Chang
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： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 51
中文關鍵詞： 分心駕駛檢測 、駕駛人在方向盤的擺放位置 、智慧手錶 、經驗模態分解 、希爾伯特-黃轉換
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：9  下載：0 
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    • 一般情況下，汽車駕駛人應使用雙手控制方向盤，否則將被視為不安全的駕駛行為。基於Aditya的論文，基於Aditya的論文，我們可以通過使用empirical mode decomposition (EMD)以及 Hilbert-Huang transform (HHT)從手錶的加速度計信號中萃取振動信息來檢測這些不安全的駕駛行為。 我們提出了一種新的方法來提高單手與雙手轉向操縱位置檢測，即為透過改用RBF-SVM和individual model和grouping model的建模方法。 我們的實驗結果顯示，通用模型中雙手檢測的準確率從75％提高到89％， individual model準確率為97％， grouping model準確率為93％。

    Normally when driving the car, the driver should hold the steering wheel with both hands. Otherwise, the action is considered as an unsafe behavior. Based on Aditya’s thesis, potentially we are able to detect those unsafe behaviors by extracting vibration information from watch’s accelerometer signal using empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT). We propose new approach to improve the accuracy value of one hand versus two hand steering handling position detection by changing the classifier using SVM with RBF kernel and modeling approach using individual model and grouping model. Our experiment result shows that the accuracy is increasing from 75% to 89% for two hand detection in universal model, 97% average in individual model, and 93% average in grouping model.

    Abstract ii ACKNOWLEDGEMENT iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vi CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Motivation 3 1.3 Research Objective 3 1.4 Thesis Structure 4 CHAPTER 2 LITERATURE REVIEW 5 2.1 Driver Safety 5 2.1.1 Driver monitoring 5 2.1.2 Driver’s hand position detection 6 2.2 Smartwatch Technology 6 2.2.1 Smartwatch for activity detection 7 2.2.2 Smartwatch for driving-related activity detection 7 2.3 Hilbert Huang Transform 9 2.3.1 Empirical Mode Decomposition 10 2.3.2 Hilbert Spectral Analysis 10 2.4 Support Vector Machine 10 2.4.1 Radiant Basis Function Kernel 11 2.5 Clustering Method 11 2.5.1 K-Means Clustering 12 2.5.2 Gaussian Mixture Model Clustering 12 2.5.3 Agglomerative Hierarchical Clustering 13 CHAPTER 3 METHODOLOGY 15 3.1 System Architecture 15 3.2 Data Collection 17 3.3 Data Preprocessing 18 3.4 Feature Extraction 19 3.5 Modeling and Testing Using SVM 21 3.5.1 Tuning SVM parameter 23 CHAPTER 4 EXPERIMENT AND RESULT 25 4.1 Feature Extraction Result Analysis 25 4.2 Driver’s Data Distribution 27 4.3 Clustering Experiments 29 4.4 Performance Evaluation 30 CHAPTER 5 CONCLUSION AND FUTURE WORKS 33 5.1 Conclusion 33 5.2 Future Works 35 REFERENCES 36

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