Simulation of gymnastics performance based on MEMS sensor

被引:0
作者
Bingxin Chen
Lifei Kuang
Wei He
机构
[1] College of Physical Education Hunan Normal University,
[2] Department of Physical Education Changsha Normal University,undefined
来源
EURASIP Journal on Advances in Signal Processing | / 2021卷
关键词
MEMS sensor; Gymnastics performance; Motion capture; Inertial measurement unit;
D O I
暂无
中图分类号
学科分类号
摘要
The development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node’s limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy.
引用
收藏
相关论文
共 75 条
[11]  
Umesh S(2018)Robust design of MEMS sensor based on piezoelectric bimorph beam Yadian Yu Shengguang/Piezoelectrics and Acoustooptics 40 33-37
[12]  
Wang DF(2018)Measurement of fluid thermal conductivity using a micro-beam MEMS sensor Int. J. Heat Mass Transfer 117 30-35
[13]  
Liu H(2018)MEMS resonator-based insulated voltage sensor withstanding higher voltage Electrical Eng. Japan 203 239-244
[14]  
Li X(2019)Design of small size and high sensitive less-invasive wireless blood pressure sensor using MEMS technology IET Circuits Devices Syst. 13 39-44
[15]  
Jakobsen H(2018)MEMS magnetic sensor with bridge-type resonator and magnetostrictive thin film Electron. Commun. Japan 101 90-95
[16]  
Aasmundtveit S(2018)Electromotive manipulator control by detection of proximity, contact, and slipping using MEMS multiaxial tactile sensor Electrical Eng. Japan 204 44-49
[17]  
Liu H(2020)High sensitivity and wide dynamic range thermoresistive micro calorimetric flow sensor with CMOS MEMS technology IEEE Sensors J. 20 4104-4111
[18]  
Xu D(2018)Calibration of low-pressure MEMS gas sensor for detection of hydrogen gas Int. J. Hydrogen Energy 43 5770-5782
[19]  
Xu DS(2020)AirCapRL: autonomous aerial human motion capture using deep reinforcement learning IEEE Robot Automation Lett 5 6678-6685
[20]  
And G(2020)Understanding violin players’ skill level based on motion capture: a data-driven perspective Cogn. Comput. 12 1-14