No abstract
With the continuous development of the economy, science and technology have achieved unprecedented development along with the improvement of the economy. The first thing to bear is the continuous popularization and application of the Internet, which drives the development of Internet technology. Many online learning platforms currently used by universities only focus on the teacher system and teaching system. Compared with other subjects, there are fewer learning materials on the Internet for gymnastics teaching, and the lack of systematic teaching is the primary problem that most schools need to overcome. The current method of online learning has changed the relatively scarce phenomenon of resources in face-to-face teaching in the past, and has excellent improvements in the organization and improvement of resources. By constructing the framework of a professional teaching resource library, collecting and properly sorting out learning materials, and at the same time designing and producing network multimedia courseware to facilitate the learning of scholars. Network teaching technology is realized by the combination of Internet and multimedia equipment. This technology can not only give play to the advantages of computers in network data transmission, but also embody a new teaching model with teachers and students as the main body. Therefore, adjusting teaching methods, building a better learning space, and using CAI courseware learning resources connected by campus network is an important way for students to make compensatory learning, and it is also the main direction for scientific and technological progress in gymnastics teaching. The experimental results prove that the online multimedia learning platform can provide students with more gymnastics learning resources, increase students' interest and time in learning; it can effectively improve the teaching quality of teachers, and use the convenient interactivity of the network to realize a resource library The rapid update of the content enables the teaching content to keep up with the needs and pace of the development of the times.
The development and progress of multi-sensor data fusion theory and method also lay the foundation for the research of human posture tracking system based on inertial sensor. This paper mainly studies the simulation of gymnastic performance based on MEMS sensors. In the preprocessing of reducing noise interference, this paper mainly uses median filter to remove signal burr. In this paper, the use of virtual character model for gymnastics performance. 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 and coordinate offset of the limb where each sensor node is located, and realizes the real-time rendering of the virtual human model by using the driver of the human model. At the same time, it controls the storage of sensor data, the driving of model and the display of graphical interface. When the gesture action is about to happen, 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 MEMS sensor. When the gesture action is completed, a signal to end the action is given to the system to mark the end of the action, so that the original signal data between the beginning and end of the gesture action can be captured. In order to ensure the normal communication between PS and PL, it is necessary to test the key interface. Because the data received by the SPI acquisition module is irregular, it is unable to verify whether the data is wrong. Therefore, the SPI acquisition module is replaced with an automatic incremental data module, and it is generated into an IP core to build a test platform for testing. The data show that the average measurement errors of x-axis displacement, Y-axis displacement, z-axis displacement and three-dimensional displacement are 8.17%, 7.51%, 9.72% and 8.7%, respectively. The results show that the MEMS sensor can accurately identify the action with high accuracy.
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.
Today, with the rapid development of information age, the communication of science and technology is getting closer to each other, and our country has begun to conduct in-depth research on WSN. This study mainly discusses the computer simulation algorithm of gymnastics formation transformation path based on wireless sensor. In this study, an improved leader follower method is designed. In the research of gymnastics formation transformation of mobile nodes in wireless sensor network environment, the traditional three types of nodes are divided into four categories according to different formation responsibilities, namely coordinator, beacon node, leader and follower. When it makes accurate positioning with the help of beacon node information, it will send the information in the form of broadcast, and then the coordinator will send the information to the host computer through the serial port for tracking display. In order to make the mobile nodes in the network keep the current gymnastics formation moving towards the target point after completing the gymnastics formation transformation, this paper uses the L - φ closed-loop control method to modify the gymnastics formation in real time. The method based on the received signal strength is used to locate the mobile node. Combined with the positioning engine in the core processor CC2431 of the mobile node, the efficient and low-energy wireless positioning can be realized. Multiple mobile nodes coordinate and control each other, and each node communicates with each other through wireless mode, and senses its own heading angle information through geomagnetic sensor, so as to judge and adjust the maintenance and transformation of the current gymnastics formation. In the process of formation transformation, the analysis shows that the maximum offset of follower2 relative to the ideal path is + 0.28M in the process of marching to the desired position in the triangle queue. This research effectively realizes the computer simulation of autonomous formation.
Today with the rapid development of the information age, the exchange of science and technology has brought closer the closeness of countries, and our country has also begun to conduct in-depth research on WSN. This research mainly discusses the computer simulation algorithm of gymnastics formation change path based on wireless sensor. In this research, an improved Leader-Follower method is designed. In the research of gymnastics formation transformation of mobile nodes in the wireless sensor network environment, the traditional three types of nodes are divided into four categories according to the different formation responsibilities, namely, coordinator, beacon node, master mobile node (Leader), slave mobile node (Follower). After it accurately locates itself with the help of the information of the beacon node, the information should be sent out in the form of broadcast, and the coordinator sends the information to the host computer through the serial port for tracking display. In order to enable the mobile nodes in the network to keep the current gymnastic formation moving toward the target point after completing the gymnastic formation transformation, this paper uses the l-φ closed-loop control method to modify the gymnastic formation in real time. The method based on the received signal strength is selected to realize the positioning of the beacon node to the mobile node, and combined with the positioning engine in the core processor CC2431 of the mobile node, efficient and low-energy wireless positioning can be realized. Multiple mobile nodes coordinate with each other to control communication between each node in a wireless manner, and sense their own heading angle information through geomagnetic sensors, so as to make judgments and adjustments on the maintenance and transformation of the current gymnastics formation. During the formation change, after analysis, it is concluded that the maximum offset of Follower2 from the ideal path in the process of traveling to the desired position in the triangular queue is + 0.28 m. This research effectively realized the computer simulation of autonomous formation.
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