Abstract
It is helpful to analyze volleyball spiking technology and improve spiking quality to extract volleyball spiking trajectory. This article studies the extraction method and teaching method of volleyball spiking trajectory based on a wireless sensor network. The acceleration sensor and gyroscope sensor are used to collect the spiking action state information of volleyball players. The collected information is transmitted to the PC terminal through the wireless sensor network, including physical layer, data link layer, network layer, transmission layer, and application layer, using the LEACH clustering routing protocol algorithm. In the PC terminal, the feedback filtering method is used to preprocess the received information and calculate the integral of each sensor node’s acceleration, connecting the spatial coordinates of each time to obtain the upper limb trajectory in three-dimensional space and realize the trajectory extraction of volleyball spike action. The experimental results show that the position error is less than 0.01 m and the speed error is less than 0.15 m/s. The application of this method in volleyball teaching can effectively improve the quality of volleyball teaching.
Highlights
With the rapid development of microelectromechanical system, computers, communication, automatic control, and artificial intelligence, a new intelligent information monitoring network, wireless sensor networks (WSN), has emerged
(3) e experimental results show that the position error is less than 0.01 m and the speed error is less than 0.15 m/s. e application of this method in volleyball teaching can effectively improve the quality of volleyball teaching
In order to verify the effectiveness of the method, a professional volleyball athlete in a university is selected as the experimental object. e acceleration sensor and gyroscope sensor are installed on the athlete’s arm, and the collected volleyball spike action signal is transmitted to the data processing module in the PC terminal by using wireless sensor network
Summary
With the rapid development of microelectromechanical system, computers, communication, automatic control, and artificial intelligence, a new intelligent information monitoring network, wireless sensor networks (WSN), has emerged. Wireless sensor network puts a large number of microsensor nodes with communication and computing capabilities into the monitoring area to sense, collect, and process the information of monitoring objects in real time. A wireless sensor network is a large-scale and resource-constrained fully distributed system [1]. Due to the perfect integration of physical information and logical world information in nature [2], and compared with traditional wireless networks, WSN have many advantages, such as high-precision monitoring of distributed processing, large-scale deployment, and application in many fields, which have attracted wide attention of military departments, industrial fields, scientific research institutions, and academic circles in the world
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