本文關(guān)鍵詞： 表面肌電信號(hào)(sEMG) 人工神經(jīng)網(wǎng)絡(luò) MYO 特征提取 手指動(dòng)作　出處：《昆明理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科技的進(jìn)步,手指的動(dòng)作識(shí)別有許多應(yīng)用,在人類交往中手部動(dòng)作占有重要地位。手指的動(dòng)作在人機(jī)交互中發(fā)展趨勢(shì)上升,對(duì)手指動(dòng)作的識(shí)別還可以幫助殘疾人和老人實(shí)現(xiàn)人機(jī)的互動(dòng),而且通過(guò)利用信號(hào)進(jìn)行神經(jīng)元分析,對(duì)患者的病情進(jìn)行診斷,制訂康復(fù)計(jì)劃等,使之成為熱門的研究項(xiàng)目。手指識(shí)別在實(shí)際生活中具有廣泛的應(yīng)用前景,然而對(duì)于手指動(dòng)作的識(shí)別研究較少,手指動(dòng)作是手勢(shì)動(dòng)作的基本單元,對(duì)于手勢(shì)動(dòng)作的研究一般采用基于電極的肌電信號(hào)來(lái)對(duì)手勢(shì)進(jìn)行識(shí)別,因此本文提出利用肘臂表面肌電信號(hào)sEMG(Surface Electromyography,肌電信號(hào)的一種)來(lái)對(duì)大拇指與食指的點(diǎn)擊,大拇指與中指的點(diǎn)擊,大拇指與無(wú)名指的點(diǎn)擊,大拇指與小拇指的點(diǎn)擊,四種手指動(dòng)作識(shí)別進(jìn)行研究。實(shí)驗(yàn)通過(guò)對(duì)表面肌電信號(hào)的分析,利用MYO手臂環(huán)來(lái)對(duì)四種手指動(dòng)作進(jìn)行數(shù)據(jù)采集,替代了傳統(tǒng)的電極深入肌肉的采集方式。通過(guò)EMGlab對(duì)MYO采集的數(shù)據(jù)進(jìn)行活動(dòng)段處理,對(duì)其信號(hào)進(jìn)行1000Hz的高頻濾波,峰值重讀,然后再對(duì)信號(hào)進(jìn)行MLT分解,得到每個(gè)頻道的模板波形,會(huì)顯示在模板面板內(nèi),最后對(duì)模板波形和整段的sEMG信號(hào)進(jìn)行平均絕對(duì)值(MAV),方差(VAR)等五種特征提取,同時(shí)作為模式識(shí)別算法的輸入?yún)?shù)。算法選擇反向神經(jīng)網(wǎng)絡(luò)(BP)來(lái)對(duì)肘臂sEMG信號(hào)的四種手指動(dòng)作進(jìn)行分類,因?yàn)锽P神經(jīng)網(wǎng)絡(luò)廣泛應(yīng)用于前人的研究中,具有高度的普適性,自適應(yīng)性強(qiáng)和結(jié)構(gòu)穩(wěn)定。實(shí)驗(yàn)結(jié)果表明BP分類器具有較高的識(shí)別準(zhǔn)確率,其對(duì)應(yīng)目標(biāo)手指動(dòng)作識(shí)別率在90.35%。通過(guò)對(duì)四種手指動(dòng)作的識(shí)別結(jié)果,設(shè)計(jì)了游戲信息控制的人機(jī)交互界面,對(duì)四種手指動(dòng)作和手指動(dòng)作組合在人機(jī)交互研究中提供可行的設(shè)計(jì)。
[Abstract]:With the development of science and technology, finger movement recognition has many applications, which plays an important role in human interaction. The recognition of finger movement can also help the disabled and the elderly to achieve human-computer interaction, and through the use of signals for neuronal analysis, diagnosis of the patient's condition, the development of rehabilitation plans and so on. Finger recognition has a wide application prospect in real life. However, there is little research on finger movement recognition. Finger action is the basic unit of gesture action. In general, the electromyography based on electrode is used to recognize the gesture. Therefore, this paper proposes the use of the elbow arm surface EMG signal sEMG(Surface myography, a kind of EMG signal, to click on the thumb and index finger. Thumb and middle finger click, thumb and ring finger click, thumb and small thumb click, four finger action recognition. The experiment through the surface EMG signal analysis. The MYO arm ring is used to collect the data of four finger movements, instead of the traditional way of collecting the electrode deep into the muscle. The data collected by MYO is processed by EMGlab. The signal is filtered with high frequency of 1000Hz, the peak value is read again, then the signal is decomposed by MLT, and the template waveform of each channel is obtained, which will be displayed in the template panel. Finally, the template waveform and the sEMG signal of the whole segment are extracted by the average absolute value of sEMG, variance and VAR. etc. At the same time, as the input parameter of the pattern recognition algorithm, the algorithm chooses the reverse neural network (BP) to classify the four finger movements of the elbow arm sEMG signal. Because BP neural network is widely used in previous research, it has high universality, strong adaptability and stable structure. The experimental results show that BP classifier has a high recognition accuracy. The recognition rate of the corresponding target finger movement is 90.35. The man-machine interactive interface of game information control is designed through the recognition results of four finger movements. The combination of four finger movements and finger movements provides a feasible design for human-computer interaction.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R741.044;TP391.41

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本文編號(hào)：1487841