Table tennis stroke technique and fitness improvement based on strength training

Abstract

Table tennis is regarded as the national ball of China, and in the actual process of competition, athletes should have both high technical and tactical levels and good physical fitness. This paper focuses on the changes in hitting skills and physical fitness of table tennis players after strength training. In order to establish a scientific training system and improve training efficiency, this paper designs a physical fitness monitoring method using big data technology. The monitoring method initially uses the backpropagation algorithm to process the physical fitness data of the athletes. To reduce the computational amount of frequent item sets, it is recommended to use the Apriori algorithm combined with the DC_Apriori algorithm for data mining on processed physical fitness data. Finally, the physical fitness training results analyzed by this fitness monitoring method were synthesized to develop a reasonable strength training program for table tennis players. The athletes were tested for changes in table tennis hitting skills and physical fitness before and after 4 weeks of strength training. By comparing with the athletes who underwent traditional physical training, it was found that the average score of table tennis batting skills of the strength training group based on big data analysis was significantly higher than the average score of the traditional physical training group. Comparative analysis of athletes’ physical fitness from four aspects: speed, strength, sensitivity, and endurance. Strength training based on big data analysis can significantly improve the physical fitness quality of table tennis players.

Keywords

• backpropagation algorithm
• dc_apriori algorithm
• apriori algorithm
• strength training
• 68m11