A Recognition Method of Athletes' Mental State in Sports Training Based on Support Vector Machine Model. (21st June 2022)

• Record Type: Journal Article
• Authors: Li, Yu
• Other Names: Shao Xuefeng Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2022(2022)
• Publication Date: 2022-06-21
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2022/1566664
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : Athletes participate in competitive competitions, and the ultimate goal is to better display their personal competitive level in the competition so as to achieve the goal of defeating their opponents and winning the competition. In all types of competitions, most matches are instantaneous, and opportunities are fleeting. The instantaneous nature and fierce competition of sports competitions require athletes who participate in sports competitions to have a high psychological quality. It can be seen that the quality of the mental state directly determines the performance of the athletes in usual training and competition. In the process of sports, if athletes can obtain real-time changes in their mental states when they encounter various situations, they can formulate more targeted and effective training or competition strategies according to the athletes' states. For the opponent, by analyzing the opponent's psychological state during exercise, the game strategy can be adjusted in real time in a targeted manner, and the probability of winning the game can be provided. Based on this background, this paper proposes to use support vector machine (SVM) to identify the mental state of athletes during exercise. This paper first collects the data of body movements and facial expressions of athletes during training or competition. Use multimodal data to train an SVM model. Output the emotional state of athletes at different stages based on test data. In order to verify the applicability of the method in this paper to the athlete subjects, several comparative models were used in the experiment to verify the performance of the used models. The experimental results show that the accuracy rate of emotion recognition obtained by this method is more than 80%. This shows that the research in this paper has certain application value.