MEMS Sensors in Tennis Teaching and Training under the Background of Big Data Intelligent Communication

Abstract

Racket sports (tennis, pawns, tennis, etc.) are currently very popular sports, and they are a very intense aerobic exercise. The climatic conditions of the competition venue, time difference factors, the size of the competition venue, background, lighting, wind direction, etc. may all have an impact on the performance of the game. In order to fully control and eliminate these risk factors, it is recommended to go to the competition site for adaptive training one month in advance. If you can participate in the prematch training camp organized by the general administration, it is of course the best choice. This research mainly discusses the development of MEMS sensors in tennis teaching and training under the background of big data intelligent communication. Using a single inertial sensor fixed on the bottom of the racket for data collection, a real-time data stream window segmentation method based on the combination of sliding window and action window is proposed. Finally, based on the above recognition method, an intelligent tennis training system software is developed, introduces the processing flow of MEMS sensor data, and gives a specific implementation plan for data preprocessing through in-depth study of the characteristics of inertial navigation data. The tennis training system collects the raw data of tennis players during the training process through the MEMS sensor hardware module placed at the bottom of the tennis racket handle, and the sensor module can upload these raw data to the smart phone terminal through wireless communication. The smart phone terminal can analyze and process the collected raw data and finally feedback the number and proportion of each technical action of the tennis player in the training process and the maximum racquet speed through the mobile phone software in real time. After the practice training, the average CTN score of the control group was 141.73, and the standard deviation of the data was 4.185. After the practice training, the average CTN score of the experimental group was 161.67, and the standard deviation was 6.042. This research will improve the efficiency of tennis teaching and training.