Abstract
In the process of sports, athletes often have aggressive behaviors because of their emotional fluctuations. This violent sports behavior has caused many serious bad effects. In order to reduce and solve this kind of public emergencies, this paper aims to create a swarm intelligence model for predicting people's sports attack behavior, takes the swarm intelligence algorithm as the core technology optimization model, and uses the Internet of Things and other technologies to recognize emotions on physiological signals, predict, and intervene sports attack behavior. The results show the following: (1) After the 50-fold cross-validation method, the results of emotion recognition are good, and the accuracy is high. Compared with other physiological electrical signals, EDA has the worst classification performance. (2) The recognition accuracy of the two methods using multimodal fusion is improved greatly, and the result after comparison is obviously better than that of single mode. (3) Anxiety, anger, surprise, and sadness are the most detected emotions in the model, and the recognition accuracy is higher than 80%. Sports intervention should be carried out in time to calm athletes' emotions. After the experiment, our model runs successfully and performs well, which can be optimized and tested in the next step.
Highlights
Sports widely exist in people’s daily life, most people are very fond of watching sports events, and sports exercise is a good way to relax and entertain
(3) Anxiety, anger, surprise, and sadness are the most detected emotions in the model, and the recognition accuracy is higher than 80%
Hu and Yin conducted research on optimal synchronous network search data extraction based on the swarm intelligence algorithm [2]
Summary
Sports widely exist in people’s daily life, most people are very fond of watching sports events, and sports exercise is a good way to relax and entertain. Based on the above background analysis, we can use the swarm intelligence model to standardize, restrain, predict, and intervene in related sports attacks and strive to minimize the bad influence. There have been a lot of information about swarm intelligence optimization in practical work and life and related references. Chantal et al [1] explored the relationship between athletes’ orientation and aggressive behavior. Hu and Yin conducted research on optimal synchronous network search data extraction based on the swarm intelligence algorithm [2]. Fister and Fister [9] used the swamp intelligence model to generate sports training plan through scientific and technological modeling and optimizing natural heuristic calculation. Petipas et al [11] defined athletes’ aggressive behavior and introduced measurement methods and influencing factors
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