發(fā)布時間：2018-06-29 00:25

  本文選題：人體基礎(chǔ)運(yùn)動 + 動態(tài)手勢識別��； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：隨著科技的進(jìn)步,移動電子設(shè)備上搭載了MEMS隕性傳感器等豐富的硬件設(shè)施,給人們的生活帶來了更多的便利,也給語音識別、圖像識別、手勢識別等新型人機(jī)交互方式提供了良好的平臺。圖像識別交互易受光線的影響、語音識別交互易被雜音干擾,于是,基于慣性傳感器的手勢交互憑借其獨(dú)特的優(yōu)勢,成為當(dāng)前研究的熱點(diǎn)。人體靜止情況下的動態(tài)手勢識別有了很大的進(jìn)展,許多學(xué)者嘗試了不同的識別方法,并驗(yàn)證了其有效性。然而,現(xiàn)階段對于人體處于運(yùn)動狀態(tài)下的手勢識別研究幾乎沒有,本文將從這個方向入手,展開對人體運(yùn)動條件下的動態(tài)手勢識別研究。本文利用諾基亞公司提供的慣性測量裝置Sensor-Box,重點(diǎn)研究步行、上下樓梯、電梯等人體基礎(chǔ)運(yùn)動條件下的動態(tài)手勢識別。本文對人體基礎(chǔ)運(yùn)動進(jìn)行了分類,并分析了人體基礎(chǔ)運(yùn)動對動態(tài)手勢的影響。結(jié)合課題研究目標(biāo),提出了三種人體運(yùn)動條件下的動態(tài)手勢識別方案。從剔除人體基礎(chǔ)運(yùn)動干擾角度,提出了基于雙慣性傳感器法和數(shù)學(xué)模型法的識別方案；從特征分類識別角度,提出了基于閾值增大法的識別方案。為了減小慣性傳感器的誤差干擾,本文建立了Sensor-Box加速度計(jì)和陀螺儀的誤差模型,并采用六位置法來標(biāo)定其確定性誤差。對加速度隨機(jī)誤差信號,采用時間序列分析法建立了ARMA模型,并用經(jīng)典卡爾曼濾波器對隨機(jī)誤差進(jìn)行了有效濾除。對于陀螺儀隨機(jī)誤差信號,采用標(biāo)準(zhǔn)Allan方差法分析識別其主要隨機(jī)誤差項(xiàng),并采用小波分析對其噪聲信號進(jìn)行了有效分離。雙慣性傳感器法通過兩個Sensor-Box同時采集人體運(yùn)動信號和動態(tài)手勢信號,然后基于相對運(yùn)動理論來剔除人體基礎(chǔ)運(yùn)動的干擾,試驗(yàn)效果滿足要求。數(shù)學(xué)模型法,針對不同類型的基礎(chǔ)運(yùn)動,提出了不同的數(shù)學(xué)模型,并給出了模型構(gòu)造方法。對于人體步行、上下樓梯等周期性明顯的信號,提出了一種建立周期信號數(shù)學(xué)模型的方法；該方法試驗(yàn)效果符合要求,算法還需完善。閾值增大法基于特征分類的方法,找出了翻轉(zhuǎn)、甩動、晃動等動態(tài)手勢的信號分類特征,建立分類器,試驗(yàn)效果良好。
[Abstract]:With the development of science and technology, mobile electronic devices are equipped with MEMS meteorite sensors and other rich hardware facilities, which bring more convenience to people's life, and also give voice recognition and image recognition. Gesture recognition and other new human-computer interaction methods provide a good platform. Image recognition interaction is easy to be affected by light, and speech recognition interaction is easily interfered by noise. Therefore, gesture interaction based on inertial sensor has become the focus of current research because of its unique advantages. There has been great progress in dynamic gesture recognition in static human body. Many scholars have tried different recognition methods and verified their effectiveness. However, there is almost no research on gesture recognition under the condition of human motion at the present stage. This paper will start with this direction and carry out the research on dynamic gesture recognition under the condition of human motion. In this paper, the sensor Box, an inertial measuring device provided by Nokia, is used to study the dynamic gesture recognition under the basic motion conditions of human body, such as walking, up and down stairs, elevators and so on. In this paper, the basic motion of human body is classified, and the influence of basic motion on dynamic gesture is analyzed. Combined with the research object, three dynamic gesture recognition schemes under human motion conditions are proposed. From the angle of eliminating human basic motion interference, this paper proposes a recognition scheme based on double inertial sensor method and mathematical model method, and from the point of view of feature classification recognition, puts forward a recognition scheme based on threshold enlargement method. In order to reduce the error interference of inertial sensor, the error model of sensor-box accelerometer and gyroscope is established in this paper, and its deterministic error is calibrated by the six-position method. The ARMA model of acceleration random error signal is established by time series analysis and the classical Kalman filter is used to filter the random error effectively. For the gyro random error signal, the main random error terms are identified by standard Allan variance method, and the noise signal is effectively separated by wavelet analysis. The dual inertial sensor method collects the human motion signal and the dynamic gesture signal simultaneously by two sensor boxes. Then based on the relative motion theory the interference of human basic motion is eliminated. The experimental results meet the requirements. In this paper, different mathematical models are proposed for different types of basic motion, and the method of model construction is given. For the periodic signals such as walking and going up and down stairs, this paper presents a mathematical model of periodic signals, the experimental results of which meet the requirements, and the algorithm needs to be improved. Based on the feature classification method, the threshold enlargement method finds out the signal classification features of the dynamic gestures such as flipping, shaking and shaking, and establishes the classifier, and the experimental results are good.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212;TN911.7

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本文編號：2080014