Design And Implementation Of MEMS Sensor-based Human Motion Capture Devices

With the rapid development of Micro-Electro-Mechanical Systems(MEMS)technology,the human body motion capture system based on MEMS sensor has the advantages of small size,high precision and low cost,which is widely used in the emerging areas,such as animated film,virtual reality and simulation training and so on.The human body action capture system based on MEMS sensor has become a research hotspot for scientific research institutions and enterprises because of its many fields of technology and broad market prospects.Therefore,it is of great theoretical and practical value to study the human body action capture system based on MEMS sensor.Firstly,based on the inertial device and inertial navigation technology,the hardware platform of human body action capture system based on MEMS sensor is designed on the basis of analyzing the demand of human motion capture.The system consists of nine-axis sensor nodes(three-axis gyroscope,three-axis accelerometer and three-axis magnetometer)and data control nodes.The controller area network(CAN)scheme is used to realize the communication between the multi-sensor node and the data control node.The XBee-WIFI wireless communication technology is used to solve the problem of stable data transmission between the data control node and the computer.Secondly,this thesis studies the error characteristics of MEMS sensors and analyzes the commonly used sensor attitude fusion algorithm.Compensating the error of the gyroscope zero deviation,scale factor and pulse noise error compensation,the experimental results show that the measurement error of the gyroscope is reduced from 0.522°/s to 0.048°/s,which improves the robustness of the MEMS gyroscope;A novel nine-position calibration algorithm is proposed for the six-position calibration method of MEMS accelerometer,which does not consider the nonlinear error of accelerometer and the crosstalk effect caused by non-orthogonal.The experimental results show that the the measurement error of the accelerometer is reduced from 0.0548 g to 0.0081 g,improving the accelerometer performance.Aiming at the nonlinear error of the magnetometer in the soft magnetic environment,a soft magnetic calibration and magnetometer error compensation algorithm based on BP neural network is proposed.The experimental results show that the error of the magnetic heading angle range from ±3° reduced to ±1.5°,improving the magnetometer anti-magnetic interference ability.In addition,analyzing the common attitude fusion algorithm,selecting the appropriate attitude fusion algorithm.Finally,based on the study of sensor node location,the human body motion attitude is calibrated,and the experiment is carried out on single-node,double-node elbow angle and upper body multi-node motion in software platform and MATLAB.The experimental results show that the mean square error of the elbow joint angle is 0.534°,which verifies the feasibility and effectiveness of the motion capture scheme designed in this paper,and realizes the function of the upper body movement capture.