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研究生: 鍾宜曄
Chung, Yi-Yeh
論文名稱: 智慧手機結合G-sensor之打瞌睡偵測系統之研發
Research and DEvelopment of Drowsiness Detection System by Using Smartphone and G-sensor
指導教授: 何宏發
Ho, Hong-Fa
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 中文
論文頁數: 116
中文關鍵詞: 打瞌睡偵測器G-sensorHaar cascade演算法瞌睡的點頭瞌睡的閉眼
英文關鍵詞: Doze (sleep) nod detector, G-sensor, Haar cascade algorithm, Doze (sleep) nod, Doze (sleep) eye closure
論文種類: 學術論文
相關次數: 點閱:116下載:23
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  • 由於紅外線車上型打瞌睡偵測器有照久了眼睛會有灼熱感的情形,故本論文的方向為開發不用紅外線且在光線不足時能做偵測打瞌睡的系統,我們用G-sensor偵測瞌睡點頭來補足光線不足時的情況。
    為了解決問題,本研究開發出打瞌睡偵測器App和G-sensor帽子,App結合智慧手機內建的攝影機用來偵測瞌睡的閉眼,App結合G-sensor帽子則可偵測瞌睡的點頭,閉眼和點頭的偵測是同時進行,所以當光線不足,無法偵測閉眼時,還有點頭偵測判斷使用者是否在打瞌睡,App偵測閉眼部分的演算法用的是Haar cascade演算法,平均處理1張影片要0.48秒,App偵測點頭部分,G-sensor帽子上裝有G-sensor、Arduino板子、藍芽模組及行動電源,G-sensor的類比資料會先透過Arduino板子轉成字串數據,再透過藍芽傳送到App做分析。
    由我們的實驗一得知,偵測閉眼的準確率為99.52%,偵測點頭的準確率為100%,由我們的實驗二得知,偵測閉眼的準確率為99.89%,偵測點頭的準確率為100%,由於兩實驗偵測點頭的準確率都為100%,故能解決光線不足時的問題。

    關鍵字:打瞌睡偵測器、G-sensor、Haar cascade演算法、瞌睡的點頭、瞌睡的閉眼

    This research has developed a system to detect doze (sleep) nod while driving, through G-sensor and without infrared. This newly invented system is not only able to detect slim eyes-brainwave-and head motion of the drivers at dim light, but capable to eliminate allergy to the eyes that are caused by infrared eye detectors, as the primary source of light for most detection equipment used by many of the eye tracking systems currently available in the market.
    To solve the problem, the research develop doze detector by using App and G-sensor cap, App combine smart phone built-in camera to detect sleepy eyes closed, App can be combined with G-sensor cap detects sleepy nod, eyes closed and nodded detection are performed simultaneously, so when lighting is poor, can not detect when eyes closed, and determine whether the user is in a nod to detect doze. To detect doze (sleep) eye closure by APP, this research has adopted Haar Cascade algorithm for measurement, with average processing time of 0.48 seconds per one photo. To effectively assess doze (sleep) nod, a G-sensor hat has been developed to cooperate with APP detector. The G-sensor cap comes with G-sensor device, Arduino board, Bluetooth module, and portable battery. G-sensor analog data will be converted into a string of data through the Arduino board, then sent via Bluetooth to the App for analysis.
    By our first experiment, detection accuracy rate of 99.52 percent with eyes closed, nodding detection accuracy was 100%. By our second experiment, detection accuracy rate of 99.89 percent with eyes closed, nodding to detect accuracy was 100%, due to the accuracy of the two experimental detection nod are 100%, it can solve the problem when lighting is poor.

    Keyword: Doze (sleep) nod detector, G-sensor, Haar cascade algorithm, Doze (sleep) nod, Doze (sleep) eye closure

    摘 要 i ABSTRACT ii 誌 謝 iv 目 錄 v 圖目錄 vii 表目錄 x 第一章 緒論 1 1.1背景與研究動機 1 1.2研究目的 2 1.3論文架構 2 第二章 文獻探討 4 2.1打瞌睡偵測器之相關文獻 4 2.1.1耳掛型 4 2.1.2測心律型 5 2.1.3腦電圖(EEG)型 6 2.1.4眼電圖裝置(EOG)型 7 2.1.5電腦視覺型 9 2.2偵測眼睛相關電腦視覺演算法 10 2.2.1影像處理型 10 2.2.2投影定位型 11 2.2.3機器學習型 12 2.3偵測眼睛的系統平台 14 2.3.1攝影機結合個人電腦型 14 2.3.2智慧型裝置型 15 2.4本篇用到人眼偵測方法 17 2.4.1人眼特徵擷取 (特徵積分圖) 17 2.4.2自適應神經網路 (AdaBoost) 19 2.4.3Haar cascade 21 2.5與打瞌睡偵測器相關專利 22 2.6偵測打瞌睡已上市或未上市產品 56 2.7 G-sensor相關文獻 58 2.7.1G-sensor介紹 58 2.7.2使用G-sensor來偵測跌倒的論文 59 2.7.3使用G-sensor來修正車牌影像歪斜的論文 61 2.7.4使用G-sensor來做動作跟蹤的論文 61 2.8 G-Sensor原理 62 第三章 打瞌睡偵測系統 67 3.1打瞌睡偵測App開發環境 67 3.2頭部傾斜偵測方法 69 3.3系統整合 70 3.3.1系統流程圖 70 3.4 App結束時附加廣告 71 3.5 App上架 72 3.6本系統硬體 74 第四章 打瞌睡偵測系統實驗 78 4.1實驗一 以影片及機械手臂模擬打瞌睡之偵測 78 4.2實驗二 打瞌睡偵測系統真人實驗 88 4.3實驗三 真實開車情況測G-sensor帽子 93 第五章 結論及未來展望 97 參考文獻 98 附 錄 一 107 自 傳 116

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