RDA-MTE: an innovative model for emotion recognition in sports behavior decision-making.

Emotional stimuli play a crucial role in sports behavior decision-making as they significantly influence individuals' responses and decisions in sports contexts. However, existing research predominantly relies on traditional psychological and behavioral methods, lacking in-depth analysis of the complex relationship between emotions and sports behavior, particularly in the integration of real-time emotion recognition and sports behavior decision-making. To address this issue, we propose a deep learning-based model, RDA-MTE, which efficiently extracts and enhances feature interaction capabilities to capture and recognize facial expressions, thereby analyzing the impact of emotional stimuli on sports behavior decision-making. This model combines a pre-trained ResNet-50, a bidirectional attention mechanism, and a multi-layer Transformer encoder to improve the accuracy and robustness of emotion recognition. Experimental results demonstrate that the RDA-MTE model achieves an accuracy of 83.54% on the FER-2013 dataset and 88.9% on the CK+ dataset, particularly excelling in recognizing positive emotions such as "Happy" and "Surprise." Additionally, the model exhibits strong stability in ablation experiments, validating its reliability and generalization capability across different emotion categories. This study not only extends research methodologies in the fields of affective computing and sports behavior decision-making but also provides significant reference for the development of emotion recognition systems in practical applications. The findings of this research will enhance understanding of the role of emotions in sports behavior and promote advancements in related fields.