Research on pre-competition emotion recognition of student athletes based on improved machine learning

Author:

Guo Chunfeng1

Affiliation:

1. College of Physical Education, Northeast Agricultural University, Harbin Heilongjiang, China

Abstract

There are currently few studies on the stress of athletes, so it is impossible to provide effective stadium guidance for athletes. Based on this, this study combines machine learning algorithms to identify athletes’ pre-game emotions. At the same time, this study obtains the data related to the research through the survey access form and obtains the physiological parameters of the athletes under stress in the experimental way and processes the physiological parameters of the athletes with the machine learning algorithm. In order to improve the efficiency of data processing, this study improves the traditional machine learning algorithm, and combines the particle optimization algorithm with the support vector machine to realize the effective recognition of the athlete’s physiological state. In addition, through the experimental method combined with the contrast method, this paper compares the performance of the improved algorithm with the traditional algorithm and combines the data analysis to analyze the test results. Finally, this study analyzes the effectiveness of the proposed algorithm by example analysis. The research shows that the proposed algorithm has better performance than the traditional algorithm and has certain practical significance and can provide theoretical reference for subsequent related research.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Load Prediction Model of Athletes’ Physical Training Competition Based on Nonlinear Algorithm Combined with Ultrasound;Contrast Media & Molecular Imaging;2022-08-24

2. Design and Implementation of Intelligent Stadium System Based on RFID Technology;The 2021 International Conference on Smart Technologies and Systems for Internet of Things;2022-07-03

3. Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data;Security and Communication Networks;2021-11-30

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