本文選題：MEMS慣性傳感器 + 橢圓擬合　； 參考：《哈爾濱工程大學(xué)》2014年碩士論文

【摘要】：基于微型慣性傳感器腿部康復(fù)動作捕捉系統(tǒng),其設(shè)計的意義在于使患者在康復(fù)訓(xùn)練時能夠?qū)崟r監(jiān)護(hù)其運動狀況,并通過再現(xiàn)本體運動來判斷訓(xùn)練姿勢是否合理。是動作捕捉技術(shù)在康復(fù)醫(yī)療領(lǐng)域中的一種應(yīng)用。文中論述了現(xiàn)有的動作捕捉系統(tǒng)的優(yōu)缺點,綜合MEMS慣性元件特點以及慣導(dǎo)系統(tǒng)的姿態(tài)解算方法,在此基礎(chǔ)上以腿部康復(fù)訓(xùn)練時的運動采集為任務(wù),對慣性式動作捕捉系統(tǒng)系統(tǒng)設(shè)計、捕捉環(huán)境的要求、使用成本進(jìn)行分析,得到采用基于MEMS慣性元件的動作捕捉方式來獲取患者腿部姿態(tài)是一種較優(yōu)越方法。研究工作主要內(nèi)容如下:(1)進(jìn)行了系統(tǒng)的結(jié)構(gòu)設(shè)計,完成節(jié)點捕捉層,節(jié)點匯聚層和節(jié)點接收層體系結(jié)構(gòu)分類,以CAN總線為基礎(chǔ),實現(xiàn)了節(jié)點捕捉層和節(jié)點匯聚層的網(wǎng)絡(luò)有線連接。以Zigbee無線通信為基礎(chǔ),實現(xiàn)了節(jié)點匯聚層和節(jié)點接收層的無線連接。實驗表明,這種網(wǎng)絡(luò)結(jié)構(gòu)可以有效完成各層間的通信。(2)以MPU9150集成慣性元件為核心結(jié)合STM32F103T8U6和CAN總線收發(fā)器VP230完成運動單元慣性數(shù)據(jù)捕捉節(jié)點的設(shè)計;基于上述所用芯片,結(jié)合CC2530F256無線SoC單片機(jī),完成匯聚節(jié)點設(shè)計;以CC2531F256無線SoC單片機(jī)為核心完成接收節(jié)點設(shè)計。實驗表明,系統(tǒng)可以完成運動信號的數(shù)據(jù)采集,處理和傳輸。(3)針對MPU9150慣性傳感器的數(shù)據(jù)采集特點,設(shè)計FIR低通濾波器對傳感器采集到的9軸數(shù)據(jù)進(jìn)行濾波處理,實現(xiàn)對高頻噪聲的抑制。并針對MPU9150內(nèi)置磁力計的靈敏度高,易受外界磁場干擾的問題,使用橢圓擬合的方法對磁力計進(jìn)行誤差補(bǔ)償。實驗表明,濾波及補(bǔ)償后的數(shù)據(jù)在平滑性和穩(wěn)定性方面具有提升。(4)以四元數(shù)法對慣性傳感器9軸數(shù)據(jù)進(jìn)行姿態(tài)解算,利用3軸加速度數(shù)據(jù)和3軸磁感應(yīng)強(qiáng)度數(shù)據(jù),實現(xiàn)姿態(tài)初始解算�；�3軸角速度數(shù)據(jù),設(shè)計卡爾曼濾波器更新四元數(shù),并結(jié)合基于高斯牛頓法,融合加速度數(shù)據(jù)和磁力計數(shù)據(jù)估計得到的四元數(shù),設(shè)計了互補(bǔ)濾波器,對捕捉節(jié)點的姿態(tài)實時更新。實驗表明,初始姿態(tài)的解算和實時姿態(tài)更新可以快速準(zhǔn)確得到捕捉節(jié)點姿態(tài)角。系統(tǒng)在上位機(jī)虛擬現(xiàn)實環(huán)境中進(jìn)行單捕捉節(jié)點和多捕捉節(jié)點的實驗,實驗表明,設(shè)計的系統(tǒng)能夠?qū)ν炔康倪\動實時進(jìn)行捕捉,數(shù)據(jù)傳輸穩(wěn)定,采集并經(jīng)過解算的數(shù)值可用于虛擬人體模型的連續(xù)驅(qū)動,滿足康復(fù)動作訓(xùn)練的使用要求。
[Abstract]:Based on the micro inertial sensor leg rehabilitation motion capture system, the significance of its design is to enable patients to monitor their movement status in real time during rehabilitation training, and to judge whether the training posture is reasonable or not by reproducing self-motion.It is an application of motion capture technology in rehabilitation medical field.This paper discusses the advantages and disadvantages of the existing motion capture system, synthesizes the characteristics of the MEMS inertial element and the attitude calculation method of the inertial navigation system. On this basis, the task of motion acquisition during leg rehabilitation training is taken as the task.The design of the inertial motion capture system, the requirement of capturing environment and the cost analysis are analyzed. It is concluded that it is a better method to obtain the leg posture of the patient by using the motion capture method based on the MEMS inertial element.The main contents of the research are as follows: (1) the structure of the system is designed, and the architecture classification of node capture layer, node convergence layer and node receiving layer is completed, which is based on CAN bus.The network wired connection between node capture layer and node convergence layer is realized.Based on Zigbee wireless communication, wireless connection between node convergence layer and node receiving layer is realized.The experimental results show that this network structure can effectively complete the communication between the layers. (2) based on the MPU9150 integrated inertial element as the core, STM32F103T8U6 and CAN bus transceiver VP230 are used to complete the design of the inertia data capture node of the motion unit.Combined with CC2530F256 wireless SoC single chip computer, the design of convergent node is completed, and the receiving node design is completed with CC2531F256 wireless SoC single chip microcomputer as the core.Experiments show that the system can complete the data acquisition, processing and transmission of motion signals. (3) according to the data acquisition characteristics of MPU9150 inertial sensors, the FIR low-pass filter is designed to filter and process the 9-axis data collected by the sensor.The suppression of high frequency noise is realized.Aiming at the problem that the MPU9150 built-in magnetometer is sensitive and easily disturbed by external magnetic field, the elliptical fitting method is used to compensate the error of the magnetometer.Experimental results show that the smoothness and stability of the filtered and compensated data are improved. (4) Quaternion method is used to calculate the attitude of 9 axis data of inertial sensor, and 3 axis acceleration data and 3 axis magnetic induction intensity data are used.The initial attitude solution is realized.Based on the 3-axis angular velocity data, the Kalman filter is designed to update the quaternion, and the complementary filter is designed based on Gao Si Newton method, which combines the acceleration data and the magnetometer data to estimate the quaternion.Update the attitude of the capture node in real time.The experimental results show that the attitude angle of the captured node can be obtained quickly and accurately by the initial attitude calculation and real-time attitude updating.The experiment of single capture node and multi-capture node in the virtual reality environment of upper computer shows that the designed system can capture the leg motion in real time and the data transmission is stable.The values collected and solved can be used to drive the virtual human model continuously and meet the requirements of rehabilitation training.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R496;TP212;TN92

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