博碩士論文 104522055 詳細資訊




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姓名 鍾智鈞(Chih-Chun Chung)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 基於慣性感測器虛擬鍵盤之設計與實作
(Design and Implementation of an IMU-based Virtual Keyboard)
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摘要(中) 在科技不斷地進步下,人類生活許多的電器用品慢慢的發生改變,朝向了輕便、便於攜帶的趨勢發展,我們在戶外使用電腦或其他電器產品的機會越來越多。人機介面作為人類與機器溝通的橋樑,人機介面須能提供更方便、更輕便的操作方式。然而笨重的實體鍵盤降低了其攜帶性,虛擬鍵盤的設計能夠讓擺脫實體鍵盤的限制。本論文設計一個基於慣性感測器的虛擬鍵盤,希望能夠利用慣性感測器的輕便性,提供一個易於攜帶的鍵盤,並且使用此虛擬鍵盤的打字方式如同一般傳統鍵盤的打字方式。本研究利用手勢辨識的方法來判斷使用者的打字動作並依照標準打字動作來操作虛擬鍵盤,此虛擬鍵盤能夠判斷26個英文字母。
摘要(英) In the continuous progress of science and technology, many of the electrical appliances slowly change in human life and towards the light, easy to carry the trend of development. The opportunities of using the computer or other electrical products in the outdoor is growing. Human-machine interface (HCI) as a bridge between human and machine communication, HCI is need to provide more convenient operation. However, the bulky physical keyboard reduces its portability, but the virtual keyboard design can get rid of the physical keyboard limitations. In this Thesis, we design a virtual keyboard based on IMU sensor to provide an easy way to carry the virtual keyboard. The typing method of this virtual keyboard typing is same as the traditional keyboard. This study uses the gesture recognition method to determine the user′s typing action. The method we use in the virtual keyboard can determine the 26 letters of English alphabet.
關鍵字(中) ★ 虛擬鍵盤
★ 類神經網路
★ 慣性感測器
★ 人機互動
★ 手勢辨識
關鍵字(英)
論文目次 摘要 i
ABSTRACT ii
Table of Contents iii
List of Figures iv
List of Tables v
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Research goal 3
Chapter 2 Materials and Methods 4
2.1 Gesture set 4
2.2 System overview 4
2.3 Data acquisition 6
2.4 Data preprocessing 7
2.5 IMU signal feature extraction 10
2.6 Neural networks 12
2.7 Display results 13
Chapter 3 Results 14
3.1 Experiments 14
3.2 Experimental results 16
Chapter 4 Discussions and Conclusions 22
References 24
參考文獻
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16. Mariano, D., et al. An accelerometer-based human computer interface driving an alternative communication system. in Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 5th ISSNIP-IEEE. 2014. IEEE. pp. 1-5.
17. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. Nature, 1986. 323(6088): pp. 533-536.
18. Tamura, H., et al., EOG-sEMG Human Interface for Communication. Computational Intelligence and Neuroscience, 2016: pp. 15-25.
指導教授 洪炯宗(Jorng-Tzong Horng) 審核日期 2017-7-21
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