本文關(guān)鍵詞：基于MA和5DT手套的上肢在線動(dòng)作捕捉系統(tǒng)　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 動(dòng)作捕捉 數(shù)據(jù)手套 虛擬現(xiàn)實(shí) 關(guān)節(jié)運(yùn)動(dòng)信息

【摘要】：人體上肢動(dòng)作捕捉技術(shù)在人機(jī)交互、疾病康復(fù)、腦機(jī)接口等諸多領(lǐng)域都有廣泛應(yīng)用。隨著相關(guān)技術(shù)的進(jìn)步,對(duì)于運(yùn)動(dòng)數(shù)據(jù)的實(shí)時(shí)性、穩(wěn)定性和精確性有著越來(lái)越高的要求。本文利用MA(Motion Analysis)光學(xué)動(dòng)作捕捉系統(tǒng)在線捕捉手臂的運(yùn)動(dòng)信息,并且對(duì)丟失標(biāo)記點(diǎn)進(jìn)行在線預(yù)測(cè);同時(shí)利用5DT數(shù)據(jù)手套來(lái)獲取手部運(yùn)動(dòng)信息。最終將手臂運(yùn)動(dòng)信息與手部運(yùn)動(dòng)信息整合起來(lái),驅(qū)動(dòng)虛擬上肢模型,實(shí)時(shí)重建人體上肢運(yùn)動(dòng)。本文主要研究?jī)?nèi)容如下:基于光學(xué)動(dòng)作捕捉系統(tǒng)計(jì)算手臂關(guān)節(jié)運(yùn)動(dòng)信息。將手臂運(yùn)動(dòng)簡(jiǎn)化為一個(gè)7自由度的模型,利用相應(yīng)的標(biāo)記點(diǎn)模塊坐標(biāo)來(lái)計(jì)算關(guān)節(jié)空間位置。針對(duì)不同的標(biāo)記點(diǎn)丟失情況,分別設(shè)定相應(yīng)的預(yù)測(cè)方法,實(shí)時(shí)預(yù)測(cè)丟失標(biāo)記點(diǎn)的空間坐標(biāo);利用貼放在皮膚表面的標(biāo)記點(diǎn)與相應(yīng)關(guān)節(jié)的固定位置關(guān)系,建立損失函數(shù),使用最小二乘法求解標(biāo)記點(diǎn)的旋轉(zhuǎn)中心,該旋轉(zhuǎn)中心即為相應(yīng)關(guān)節(jié)的空間位置�；跀�(shù)據(jù)手套計(jì)算各個(gè)手指關(guān)節(jié)的空間位置。本文采用了 26自由度的手部運(yùn)動(dòng)模型,將手指各個(gè)自由度的旋轉(zhuǎn)角度與數(shù)據(jù)手套傳感器數(shù)值對(duì)應(yīng)起來(lái)。利用傳感器數(shù)據(jù)計(jì)算手指關(guān)節(jié)自由度的旋轉(zhuǎn)角度。最后通過(guò)前向運(yùn)動(dòng)學(xué)方法計(jì)算手指關(guān)節(jié)位置。統(tǒng)一手臂與手部坐標(biāo)并實(shí)時(shí)繪制上肢運(yùn)動(dòng)。利用手部和前臂的標(biāo)記點(diǎn)計(jì)算得到手腕坐標(biāo)。利用手部的三個(gè)標(biāo)記點(diǎn)計(jì)算得到手的方向。根據(jù)手腕坐標(biāo)和手的方向?qū)⑹值倪\(yùn)動(dòng)映射到手臂上,得到完整的人體上肢骨骼關(guān)節(jié)運(yùn)動(dòng)信息。根據(jù)這些信息,利用OpenGL實(shí)時(shí)繪制一個(gè)可以與真實(shí)人體上肢同步運(yùn)動(dòng)的虛擬上肢。通過(guò)計(jì)算,MA丟失標(biāo)記點(diǎn)的預(yù)測(cè)誤差基本集中在0.5mm以內(nèi),關(guān)節(jié)位置與標(biāo)記點(diǎn)距離變化在1mm左右,在10分鐘的長(zhǎng)時(shí)間運(yùn)行過(guò)程中系統(tǒng)可以一直穩(wěn)定在線運(yùn)行,證明該系統(tǒng)可以實(shí)時(shí)在線的得到上肢運(yùn)動(dòng)數(shù)據(jù)。
[Abstract]:Human upper limb motion capture technology has been widely used in human-computer interaction, disease rehabilitation, brain-computer interface and many other fields. With the progress of related technology, real-time motion data. There are more and more requirements for stability and accuracy. This paper uses the MA(Motion Analysis) optical motion capture system to capture the motion information of the arm online. And the lost mark points are predicted on line. At the same time, 5DT data gloves are used to obtain the hand movement information. Finally, the arm motion information and the hand motion information are integrated to drive the virtual upper limb model. The main contents of this paper are as follows: based on the optical motion capture system, the motion information of the arm joint is calculated, and the arm motion is simplified into a 7 degree of freedom model. The joint space position is calculated by using the corresponding marker point module coordinates, and the corresponding prediction methods are set up to predict the spatial coordinates of the missing mark points in real time. The loss function was established by using the fixed position relationship between the labeled points attached to the skin surface and the corresponding joints, and the rotation center of the mark points was solved by the least square method. The center of rotation is the space position of the corresponding joint. The spatial position of each finger joint is calculated based on the data glove. In this paper, the hand motion model with 26 degrees of freedom is adopted. The rotation angle of each degree of freedom of the finger is matched with the data glove sensor. The rotation angle of the degree of freedom of the finger joint is calculated by the sensor data. Finally, the position of the joint of the finger is calculated by the method of forward kinematics. The coordinate of arm and hand is unified and the movement of upper limb is plotted in real time. The coordinate of wrist is calculated by using the marking point of hand and forearm. The direction of hand is calculated by using the three marking points of hand. According to the direction of wrist and hand, The movement of the hand maps to the arm. According to these information, a virtual upper limb which can move synchronously with real human upper limb can be drawn by OpenGL in real time. The prediction error of MA missing mark point is mainly within 0.5 mm, and the distance between joint position and mark point is about 1 mm. It is proved that the system can get the upper limb motion data online in real time.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;R49

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本文編號(hào)：1379840