使用一台智能手錶在有限的數據下檢測方向盤位置

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姓名

尤蘇福(Moh. Bagus Luqman Yusup) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用一台智能手錶在有限的數據下檢測方向盤位置
(Detecting Steering Handling Position Using One Smartwatch with limited data)

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摘要(中)

超過百分之十的交通事故都起因於不專心的駕駛。當駕駛人分心於其他事情時，雙手仍須握在方向盤上，否則就是不安全的駕駛。根據Setyan的論文，將智慧手錶中三軸加速器的震動幅度資訊套用 empirical decomposition 和 Hilbert-Huang 轉換，不安全的駕駛行為就可以被偵測出來。據文中所述，以上的方法可以在每個駕駛人資料集的70%中達到97%的平均單一模型正確率，以及93.4%的grouping model正確率。在此研究中，我們提出一個提升 grouping model 正確率的方法去計算每個group的權重，而非選擇單一個 group 並以 leave one person test method 驗證。同時，我們也建立了一個新的獨立模型來訓練少量的資料。實驗成果顯示，在同樣的測試方法下我們提出的方法達到了93.6%的平均正確率；在2%的訓練資料中達到80%的平均單一模型正確率；在5%的訓練資料中達到了90%的平均單一模型正確率。
關鍵字: 駕駛者行為辨識、分心駕駛、智慧手錶、有限資料、希爾伯特-黃轉換

摘要(英)

More than 10 percent of car accidents cause by a distracted driver, Distraction occurs when driver divert their attention from the driving task, the driver should hold the steering wheel with both two hands during the driving otherwise, it will be classified as unsafe driving behavior. Based on Setyan’s thesis, he can detect the driver′s unsafe driving behavior by extracting vibration information from a smartwatch accelerometer sensor using empirical decomposition and Hilbert-Huang transform, and he claims using 70% of each driver dataset he can obtain 97% individual model accuracy in average, and 93.4% average in grouping model. In this research we propose an approach to improve the group model accuracy by calculate weight every group instead of selecting one group and validate the model with leave one person test method, we also a create new individual model to handle the small data training. Our experiment result shows an average accuracy up to 93.6% in group model with the same testing method, 80% on average for the individual model with only 2% of data training, and 90% on average for the individual model with 5% data training.
Keywords: driver’s hand detection, distracted driving, smartwatch, limited data, Hilbert-Huang transform.

關鍵字(中)

★ 駕駛者行為辨識
★ 分心駕駛
★ 有限資料
★ 希爾伯特-黃轉換

關鍵字(英)

★ driver’s hand detection
★ distracted driving
★ limited data
★ Hilbert-Huang transform
★ smartwatch

論文目次

中文摘要 i
English Abstract ii
Acknowledgments iii
Table of Contents iv
List of Figures vii
List of Tables viii
I Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Research objective 4
1.4 Thesis Structure 5
II Literature Review 6
2.1 Driver Safety 6
2.1.1 Driver Distraction Monitoring 6
2.1.2 Driver’s Hand Position Detection 7
2.2 Smartwatches Technology 8
2.2.1 Smartwatches for activity detection 9
2.2.2 Smartwatches for driving activity detection 9
2.3 Vibration Analysis 10
2.3.1 Hilbert-Huang Transform 11
2.4 Support Vector machine 11
2.5 Gaussian Mixture Model 12
2.6 Logistic Regression 13
III Methodology 14
3.1 System Architect 14
3.2 Data Collection 16
3.3 Data Preprocessing 18
3.4 Feature Extraction 19
3.5 Training and Testing Classifier Model 21
3.5.1 Split Training Data and Testing Data 21
3.5.2 Modeling and Testing 21
3.5.3 Tuning SVM Hyperparameter 24
IV Experiment and Result 26
4.1 Extracted Feature Analysis 26
4.2 Experiment Methods 28
4.2.1 Preleminary Experiment : Comparing old car data with new car data 28
4.2.2 Grouping Model 29
4.2.3 Individual Model 31
V Conclusion and Future Work 35
5.1 Conclusion 35
5.2 Future Works 37
References 38

參考文獻

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指導教授

梁德容張欽圳(Deron Liang Chin-Chun Chang)

審核日期

2020-7-28

推文