使用卷積神經網路之眼寫符號圖像辨識法

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：34

、訪客IP：3.15.171.202

姓名

張右新(Yu-Hsin Chang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

使用卷積神經網路之眼寫符號圖像辨識法
(Pictorial Method with Convolutional Neural Network for Eye-writing Symbol Recognition)

相關論文

★ 電子式基因序列偵測晶片之原型	★ 眼動符號表達系統之可行性研究
★ 利用網印碳電極以交流阻抗法檢測糖化血紅素	★ 電子式基因序列偵測晶片可行性之研究
★ 電腦化肺音擷取系統	★ 眼寫鍵盤和眼寫滑鼠
★ 眼寫電話控制系統	★ 氣喘肺音監測系統之可行性研究
★ 肺音聽診系統之可行性研究	★ 穿戴式腳趾彎曲角度感測裝置之可行性研究
★ 注音符號眼寫系統之可行性研究	★ 英文字母眼寫系統之可行性研究
★ 數位聽診器之原型	★ 使用角度變化率為基準之心電訊號壓縮法
★ 電子式基因微陣列晶片與應用電路研究	★ 電子聽診系統應用於左右肺部比較之臨床研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

Amyotrophic lateral sclerosis (ALS)是一種神經病變疾病，也被稱作Lou Gehrig‘s disease這種疾病特徵是會使腦中的運動神經不斷退化，也就是我們俗稱的漸凍人。患有ALS的病人四肢及軀幹的肌肉會逐漸地癱瘓麻痺甚至無力，同時也會慢慢喪失講話的功能，因此嚴重者會無法使用他們的肢體與口語溝通的能力。
本研究的宗旨就是希望可以利用眼電圖法(ElectrooculoGraphy , EOG)做為眼動訊號的偵測來建構一套”眼寫系統”，並利用CNN網路來提高其辨識率。系統主要可以分成硬體和軟體兩大部分，而軟體的部分主要又可以分成：校準、雙眨眼偵測、符號分類三大功能，首先利用EOG訊號擬合出一個用來重建眼睛移動軌跡的轉移函數，有了這個轉移函數我們可以解決在時域上眼寫時符號的歪寫和比例大小問題，再者透過以訓練好的雙眨眼神經網路在時域上來進行雙眨眼偵測做為眼寫的開始，把截取之眼寫訊號轉換成29  29 pixel的二維圖像並強化圖像特徵，最後以卷積神經網路(CNN)來分類26個英文大寫字母、10個數字和4個命令符號(刪除、空白、換行、結束)。本論文利用CNN之圖像法來分類雙眨眼和字符大大的提高了其個人書寫的辨識率平均達到95%以上。

摘要(英)

Amyotrophic lateral sclerosis (ALS) is a neuropathic disease. This disease is characterized by gradual degeneration of motor nerves in the brain, thus patients with ALS will experience progressive numbness and even weakness in the muscles of the extremities and trunks, and will slowly lose their speaking ability. Therefore, severe patients will not be able to move their body and communicate with others.
The purpose of this study is to use the electro-oculogram method in an "eye-writing system" and use the CNN (convolutional neural network) network to improve its recognition rate. The main algorithm can be divided into three parts: calibration, double-blink detection, and symbol classification.
First, the EOG signal at nine reference points is fitted to a transfer function to reconstruct the eye movement plane. With this transfer function, we can solve the problem of skewing and unequal scaling of the symbols in the time domain. Secondly, the trained CNN will detect double blinking from the EOG signal in the time domain to mark the beginning of the eye-writing of a symbol. Thirdly, the intercepted eye writing signal is converted into a 29 × 29 pixel two-dimensional image and the image features are enhanced. Finally, the CNN is used to classify 26 English letters,10 numbers and 4 command symbols (delete, blank, line feed, end).
The CNN used in this study to classify double blinking and characters greatly improves the recognition of eye-writing symbols, with an average recognition rate over 95%..

關鍵字(中)

★ 眼電圖
★ 卷積神經網路
★ 眼動訊號眨眼偵測
★ 眼寫系統

關鍵字(英)

★ Electro-oculography(EOG)
★ Convolutional Neural Network (CNN)
★ EOG blinking detection
★ Eye-writing

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 xi
第一章緒論 1
1.1研究動機 1
1.2文獻回顧 2
1.3論文架構 5
第二章研究背景與知識 6
2.1 其他記錄眼動的方法 6
2.2 眼電圖法(Electrooculography，EOG) 8
2.3 眼電圖波形介紹 11
第三章眼寫系統方法與架構 13
3.1 系統流程 13
3.2硬體電路架構 14
3.2.1 儀表放大器 16
3.2.2 右腳驅動電路 17
3.2.3 濾波電路 18
3.2.4 反向放大電路 20
3.2.5 稽納定電壓電路 21
3.2.6 加法電路 21
3.2.7 微處理器(Arduino)[9] 22
3.3軟體演算法 24
3.3.1 移動平均濾波器(simple moving average, SMA) 27
3.3.2 多點校準(Multiple-point calibration, MPC) 29
3.3.3 雙眨眼訊號處理(Double-blink detection) 33
3.3.4 正規化並轉成圖像(Normalization and Image conversion) 41
3.3.5 特徵增強(Feature enhancement) 43
3.3.6 卷積神經網路(CNN)架構與訓練 45
第四章實驗環境與實驗步驟 52
4.1實驗環境 52
4.2測試步驟 54
第五章實驗結果與討論 56
5.1實驗結果 56
5.1.1訓練資料和測試資料的準確率和曲線圖 56
5.1.2實際眼寫準確率 59
5.2.3眼寫速度 60
5.2討論 61
第六章結論與未來展望 62
參考資料 63
附錄 A 66

參考文獻

[1] D. Hirtz, D. Thurman, K. Gwinn-Hardy, M. Mohamed, A. Chaudhuri, and R. J. N. Zalutsky, "How common are the “common” neurologic disorders?," vol. 68, no. 5, pp. 326-337, 2007.
[2] T. Yagi, Y. Kuno, K. Koga, and T. Mukai, "Drifting and blinking compensation in electro-oculography (EOG) eye-gaze interface," in Systems, Man and Cybernetics, 2006. SMC′06. IEEE International Conference on, 2006, vol. 4, pp. 3222-3226: IEEE.
[3] M. Y. Hesna Özbek Ülkütaş, "Computer based eye-writing system by using EOG," Bodrum, Turkey, 15-18 Oct. 2015.
[4] A. Lopez, F. Ferrero, D. Yanguela, C. Alvarez, and O. Postolache, "Development of a Computer Writing System Based on EOG," Sensors (Basel), vol. 17, no. 7, Jun 26 2017.
[5] K.-R. Lee, W.-D. Chang, S. Kim, and C.-H. Im, "Real-time “eye-writing” recognition using electrooculogram," IEEE Transactions on Neural Systems Rehabilitation Engineering, vol. 25, no. 1, pp. 37-48, 2017.
[6] M. Merino, O. Rivera, I. Gómez, A. Molina, and E. Dorronzoro, "A method of EOG signal processing to detect the direction of eye movements," in Sensor Device Technologies and Applications (SENSORDEVICES), 2010 First International Conference on, 2010, pp. 100-105: IEEE.
[7] 儀表放大器. https://zh.wikipedia.org/wiki/%E5%84%80%E8%A1%A8%E6%94%BE%E5%A4%A7%E5%99%A8.
[8] T. INSTRUMENTS. (2019). INA12x Precision, Low-Power Instrumentation Amplifiers,http://www.ti.com/lit/ds/symlink/ina128.pdf.
[9] L. H. Goldberg. (2012-02-01). Arduino 的類比功能：如何運用在新設計中,https://www.digikey.tw/zh/articles/techzone/2012/feb/arduinos-analog-functions-how-to-use-them-in-your-next-design.
[10] MathWorks. https://www.mathworks.com/products/matlab.html.
[11] 張嘉安. (2006,01,01). 眼寫鍵盤和眼寫滑鼠 Eye-writing Keyboard and Eye-writing Mouse.
[12] T. Yagi, Y. Kuno, K. Koga, and T. Mukai, "Drifting and blinking compensation in electro-oculography (EOG) eye-gaze interface," in 2006 IEEE International Conference on Systems, Man and Cybernetics, 2006, vol. 4, pp. 3222-3226: IEEE.
[13] L. J. E. Lindstrom, "Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals," vol. 10, no. 4, pp. 341-356, 1970.
[14] F. Simini, A. Touya, A. Senatore, and J. Pereira, "Gaze tracker by electrooculography (EOG) on a head-band," in 2011 10th International Workshop on Biomedical Engineering, 2011, pp. 1-4: IEEE.
[15] X. Kong and G. F. Wilson, "A new EOG-based eyeblink detection algorithm," Behavior Research Methods, Instruments, & Computers, vol. 30, no. 4, pp. 713-719, 1998.
[16] M. S. Reddy, B. Narasimha, E. Suresh, and K. S. Rao, "Analysis of EOG signals using wavelet transform for detecting eye blinks," in 2010 International Conference on Wireless Communications & Signal Processing (WCSP), 2010, pp. 1-4: IEEE.
[17] MathWorks, "Deep Learning,https://www.mathworks.com/solutions/deep-learning/convolutional-neural-network.html."
[18] 台大電機系李宏毅老師. (2016). ML Lecture 6: Introduction of Deep Learning,http://violin-tao.blogspot.com/2017/07/ml-introduction-of-deep-learning.html.
[19] S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv preprint arXiv:1502.03167, 2015.
[20] 台大電機系李宏毅老師. (2017). Batch Normalization,https://www.youtube.com/watch?v=BZh1ltr5Rkg.

指導教授

蔡章仁(Jang-Zern Tsai)

審核日期

2019-8-22

推文