Simulation of gymnastics performance based on MEMS sensor

Bingxin Chen,Lifei Kuang,Wei He

doi:10.1186/s13634-021-00760-4

Abstract

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.

Highlights

At present, new forms of human-computer interaction terminals such as smart clothing, smart accessories, and other wearable computing systems such as mobile Internet, cloud computing, and the Internet of Things have become fashionable consumer electronic products
The cross integration of wearable computing and emotional computing, physiological computing, and social computing will enrich the research content of big data, human-computer interaction, and intelligent sensing; wearable computing is deeply applied to health care, digital media, mobile communications, and textiles and clothing will bring new industrial chains
The motion capture system based on microelectromechanical system (MEMS) sensors completely relies on inertial sensors for the collection of human motion data

Summary

Introduction

New forms of human-computer interaction terminals such as smart clothing, smart accessories, and other wearable computing systems such as mobile Internet, cloud computing, and the Internet of Things have become fashionable consumer electronic products. Temperature, and time material properties of the molding compound used in MEMS devices He used the dynamic mechanical analysis (DMA) method to conduct a series of stress relaxation tests to obtain the viscoelastic properties of the molding compound. He used experimental data to perform numerical simulations and estimated the temperature and humidity stress of the MEMS sensor device under temperature cycles. In order to improve the control bandwidth, he optimized the structural topology of the TSAY elliptical mirror to reduce the moment of inertia while maintaining the surface flatness He uses flexible hinges to achieve a wide range of angles. The posture calculation is performed on the processed sensor signal, and the posture angle calculated by the angular velocity signal is fused and corrected with the posture angle signal calculated by the acceleration signal, and a high-precision attitude angle is given

MEMS sensor and motion capture technology

Gymnastics performance simulation experiment

Results and discussion

Conclusions