眼動(dòng)信號(hào)的提取與分類識(shí)別研究
發(fā)布時(shí)間：2018-09-18 10:13
【摘要】：人機(jī)交互技術(shù)是連接人與計(jì)算機(jī)或其他電子設(shè)備的橋梁。近幾年來,隨著科學(xué)技術(shù)的快速發(fā)展,人機(jī)交互技術(shù)也因此取得了更大的發(fā)展,不斷的向更自然、更和諧、更便利的方向發(fā)展。人作為生物體自身包含了各種各樣的生物電信號(hào),例如:腦電信號(hào)、肌電信號(hào)、心電信號(hào)、眼動(dòng)信號(hào)等。人們可以對(duì)這些信號(hào)進(jìn)行采集并進(jìn)行有效的破譯,然后再賦予這些信號(hào)特定的含義,這樣就可以為人類所用。利用這些豐富的生命體征信息資源開發(fā)出更加具有交互性的以“人類為主導(dǎo)”的人機(jī)交互系統(tǒng),逐漸成為科研工作者和相關(guān)領(lǐng)域?qū)＜也粩嗵剿鞯膯栴}。眼動(dòng)信號(hào)是一種由眼部運(yùn)動(dòng)而引起眼部周圍電勢(shì)發(fā)生變化的生物電信號(hào)。隨著眼球運(yùn)動(dòng),眼動(dòng)信號(hào)也會(huì)發(fā)生變化,眼動(dòng)信號(hào)的波形特點(diǎn)與眼球運(yùn)動(dòng)的方式有直接的對(duì)應(yīng)關(guān)系;并且,眼動(dòng)信號(hào)具有幅值較高、波形便于檢查、處理容易等優(yōu)勢(shì)。因此,將眼動(dòng)信號(hào)作為開發(fā)基于眼動(dòng)信號(hào)的人機(jī)交互技術(shù)是解決特殊人士人機(jī)交互問題的新方向。本文主要研究了眼動(dòng)信號(hào)的采集和預(yù)處理、端點(diǎn)檢測(cè)、有效眼動(dòng)信號(hào)的提取、特征提取,并分別用BP算法、SVM算法、DTW算法對(duì)眼動(dòng)信號(hào)進(jìn)行模式識(shí)別研究。其中,DTW算法有效的解決眼動(dòng)信號(hào)因人而異、長短不一的問題,并提高了眼動(dòng)信號(hào)的識(shí)別率,取得了較好的效果,為基于眼動(dòng)信號(hào)的人機(jī)交互系統(tǒng)設(shè)計(jì)奠定了一定的研究基礎(chǔ)。文章主要完成了以下幾項(xiàng)主要工作:1.眼動(dòng)信號(hào)的采集和預(yù)處理:設(shè)計(jì)了眼動(dòng)信號(hào)采集實(shí)驗(yàn)并且對(duì)多名受試者進(jìn)行數(shù)據(jù)采集,獲取了大量的原始眼動(dòng)信號(hào)數(shù)據(jù)。并通過硬件電路對(duì)原始數(shù)據(jù)進(jìn)行初步的濾波、降噪處理。2.有效信號(hào)的提取:通過對(duì)信號(hào)進(jìn)行軟件濾波、分幀、加窗、計(jì)算短時(shí)能量以及端點(diǎn)檢測(cè)等處理后,提取可靠的有效信號(hào)。3.眼動(dòng)信號(hào)的特征提取及分析:根據(jù)有效信號(hào)的波形特點(diǎn),分析可用于模式識(shí)別的特征值,提取基于波形特點(diǎn)的眼動(dòng)信號(hào)特征值。4.分別利用BP算法、SVM算法以及DTW算法對(duì)眼動(dòng)信號(hào)進(jìn)行分類識(shí)別研究,討論三種模式識(shí)別方法的優(yōu)劣性。
[Abstract]:Human-computer interaction technology is a bridge between human beings and computers or other electronic devices. In recent years, with the rapid development of science and technology, human-computer interaction technology has also made greater development, constantly to more natural, more harmonious, more convenient direction of development. As an organism, human beings contain a variety of bioelectrical signals, such as EEG, EMG, ECG, eye movement and so on. These signals can be collected and deciphered effectively, and then given a specific meaning, so that they can be used by human beings. Using these abundant vital sign information resources to develop a more interactive human-computer interaction system, which is gradually becoming a problem that researchers and experts in related fields continue to explore. Eye movement signal is a kind of bioelectric signal caused by eye movement. With the eye movement, eye movement signal will also change, the characteristics of eye movement signal waveform and eye movement mode have a direct corresponding relationship; moreover, eye movement signal has the advantages of high amplitude, easy to check the waveform, easy to process, and so on. Therefore, it is a new direction to develop the human-computer interaction technology based on eye movement signal to solve the problem of human-computer interaction by special people. In this paper, the acquisition and preprocessing of eye movement signal, endpoint detection, extraction of effective eye movement signal and feature extraction are mainly studied, and the pattern recognition of eye movement signal is studied using BP algorithm. The DTW algorithm can effectively solve the problem that eye movement signal varies from person to person, and improves the recognition rate of eye movement signal, and achieves good results, which lays a certain research foundation for the design of man-machine interaction system based on eye movement signal. The article mainly completed the following main work: 1. The acquisition and preprocessing of eye movement signal: the experiment of eye movement signal acquisition was designed and a large number of original eye movement signal data were acquired. And through the hardware circuit to the original data preliminary filtering, noise reduction processing. 2. Effective signal extraction: after processing such as software filtering, framing, windowing, calculating short-time energy and endpoint detection, the reliable effective signal .3is extracted. Feature extraction and analysis of eye movement signals: according to the waveform characteristics of effective signals, the eigenvalues that can be used in pattern recognition are analyzed, and the eigenvalues of eye movement signals based on waveform characteristics are extracted. BP algorithm and DTW algorithm are used to classify and recognize eye movement signals, and the advantages and disadvantages of the three pattern recognition methods are discussed.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN911.7
本文編號(hào)：2247587
[Abstract]:Human-computer interaction technology is a bridge between human beings and computers or other electronic devices. In recent years, with the rapid development of science and technology, human-computer interaction technology has also made greater development, constantly to more natural, more harmonious, more convenient direction of development. As an organism, human beings contain a variety of bioelectrical signals, such as EEG, EMG, ECG, eye movement and so on. These signals can be collected and deciphered effectively, and then given a specific meaning, so that they can be used by human beings. Using these abundant vital sign information resources to develop a more interactive human-computer interaction system, which is gradually becoming a problem that researchers and experts in related fields continue to explore. Eye movement signal is a kind of bioelectric signal caused by eye movement. With the eye movement, eye movement signal will also change, the characteristics of eye movement signal waveform and eye movement mode have a direct corresponding relationship; moreover, eye movement signal has the advantages of high amplitude, easy to check the waveform, easy to process, and so on. Therefore, it is a new direction to develop the human-computer interaction technology based on eye movement signal to solve the problem of human-computer interaction by special people. In this paper, the acquisition and preprocessing of eye movement signal, endpoint detection, extraction of effective eye movement signal and feature extraction are mainly studied, and the pattern recognition of eye movement signal is studied using BP algorithm. The DTW algorithm can effectively solve the problem that eye movement signal varies from person to person, and improves the recognition rate of eye movement signal, and achieves good results, which lays a certain research foundation for the design of man-machine interaction system based on eye movement signal. The article mainly completed the following main work: 1. The acquisition and preprocessing of eye movement signal: the experiment of eye movement signal acquisition was designed and a large number of original eye movement signal data were acquired. And through the hardware circuit to the original data preliminary filtering, noise reduction processing. 2. Effective signal extraction: after processing such as software filtering, framing, windowing, calculating short-time energy and endpoint detection, the reliable effective signal .3is extracted. Feature extraction and analysis of eye movement signals: according to the waveform characteristics of effective signals, the eigenvalues that can be used in pattern recognition are analyzed, and the eigenvalues of eye movement signals based on waveform characteristics are extracted. BP algorithm and DTW algorithm are used to classify and recognize eye movement signals, and the advantages and disadvantages of the three pattern recognition methods are discussed.
【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN911.7
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 梁秀波;張順;李啟雷;張翔;耿衛(wèi)東;;運(yùn)動(dòng)傳感驅(qū)動(dòng)的3D直觀手勢(shì)交互[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2010年03期
2 孔俊其;王輝;張廣泉;;基于加速度識(shí)別的姿態(tài)交互研究[J];蘇州大學(xué)學(xué)報(bào)(工科版);2009年02期
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