Psychological Training Method for Table Tennis Players Using Deep Learning

Abstract

In China, table tennis has always been one of the most popular sports, with small field restrictions and it is simple and fun to play. But what is unknown is that with China’s major table tennis events thriving, the psychological pressure of table tennis players’ training is also increasing. However, the existing training methods obviously do not pay enough attention to psychological training, and there is no complete system for psychological training. Although the current dynamic heart rate measurement method can play a certain role in the detection, there is still the disadvantage of low detection accuracy. The heart rate is an important sign of life, and the monitoring of the heart rate of table tennis players should be strengthened, reflecting the functional state of the athletes’ heart, and facilitating the more intuitive adjustment of the psychological training method of athletes. The heart rate measurement method based on face recognition contains a plate content that is rich. Through deep learning face recognition, the heart rate measurement method has high calculation efficiency, can effectively eliminate the influence of other external environmental factors, can use video recording, and can use face recognition and physiological parameters’ quantification to monitor athletes’ heart rate changes in real time; the visible artificial intelligence auxiliary diagnosis potential is huge. To this end, this paper aims to provide effective suggestions for the psychological training of table tennis players with deep learning as the technical support. In response to this, the aim of this paper was to design a deep learning-based face recognition heart rate measurement method that judges the psychological fluctuations of athletes through changes in heart rate. We conducted a questionnaire survey with the table tennis players of the Hunan team as the object of investigation, so as to understand the source of the psychological pressure of athletes and make reasonable suggestions. The experimental results of this paper show that the heart rate error of the video heart rate measurement algorithm is within 3% in the calm state and within 4% in the post-exercise state. This can effectively measure psychological fluctuations, and through investigation and research, it can provide an effective method for the psychological training of table tennis players.