How to play under pressure: EEG monitoring of mental activation training in a professional tennis player

The influence of psychological change factors of tennis training strategy using optimized recurrent neural network and artificial intelligence

Due to the specialization of tennis technical training, the teaching focus of tennis teaching has gradually shifted to the psychological skills training of tennis players. This work addresses the impact of psychological factors on tennis players' insufficient concentration in teaching and training on match results. It discusses the psychological changes' influencing factors in tennis training strategies and analyzes the current psychological changes that are easy to occur in tennis teaching. The Recurrent Neural Network (RNN) can simulate the human brain's information processing and learning process to establish models to study human psychological changes. To explore the influence of psychological changes on tennis training, artificial intelligence technology is combined to optimize the performance of RNN, and a prediction model of psychological distress in tennis training is constructed. Additionally, a questionnaire is applied to compare the sports state of tennis players before and after the psychological regulation intervention. The findings demonstrate that following psychological regulation, 73 % of players perform as usual, 20 % present exceptionally well, and 7 % do not perform as well as usual. These results indicate an improvement compared to previous performances, highlighting the efficacy of psychological regulation supported by optimized RNN under AI assistance. This study aims to foster a consistently positive psychological state among tennis players during daily training and competitions, ensuring that their competitive performance levels remain normal or even exceed their usual standards.

The Use of Neurofeedback in Sports Training: Systematic Review

Biofeedback training is a method commonly used in various fields of life, for example, in medicine, sports training or business. In recent studies, it has been shown that biofeedback, and neurofeedback, can affect the performance of professional athletes. Training based on the neurofeedback method includes exercising the brain waves. The aim of the article is to evaluate the influence of neurofeedback training on the physical fitness of professional athletes representing various sports disciplines, such as judo, volleyball and soccer. Based on 10 scientific papers from various sources, including PubMed, the latest research on neurofeedback and its impact on athletes has been reviewed. On the basis of the literature review from 2012 to 2022 on the neurofeedback method in sports training, it can be stated that this type of practice has a significant impact on physical fitness and sports performance. This review comprised 10 research studies with 491 participants in the neurofeedback groups, and 62 participants in the control group. Two reviewers independently extracted data and evaluated the quality of the studies utilising the PEDro scale. Properly planned and conducted neurofeedback training affects stimulation and improvement of many variables (reducing stress levels, increasing the ability to self-control physiological factors, enhancing behavioural efficiency and meliorating the speed of reaction to a stimulus).

Toward Detecting the Zone of Elite Tennis Players Through Wearable Technology

Wearable devices fall short in providing information other than physiological metrics despite athletes' demand for psychological feedback. To address this, we introduce a preliminary exploration to track psychological states of athletes based on commercial wearable devices, coach observations and machine learning. Our system collects Inertial Measuring Unit data from tennis players, while their coaches provide labels on their psychological states. A recurrent neural network is then trained to predict coach labels from sensor data. We test our approach by predicting being in the zone, a psychological state of optimal performance. We conduct two experimental games with two elite coaches and four professional players for evaluation. Our learned models achieve above 85% test accuracy, implying that our approach could be utilized to predict the zone at relatively low cost. Based on these findings, we discuss design implications and feasibility of this approach by contextualizing it in a real-life scenario.