Zhang Sheng'ao
College of Physical Education, Huazhong University of Science and Technology, Wuhan, China.
Front Neurosci. 2024 Nov 14;18:1466013. doi: 10.3389/fnins.2024.1466013. eCollection 2024.
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.
情绪刺激在运动行为决策中起着至关重要的作用，因为它们会显著影响个体在运动情境中的反应和决策。然而，现有研究主要依赖于传统的心理学和行为学方法，缺乏对情绪与运动行为之间复杂关系的深入分析，特别是在实时情绪识别与运动行为决策的整合方面。为了解决这一问题，我们提出了一种基于深度学习的模型RDA-MTE，该模型能够有效地提取并增强特征交互能力，以捕捉和识别面部表情，从而分析情绪刺激对运动行为决策的影响。该模型结合了预训练的ResNet-50、双向注意力机制和多层Transformer编码器，以提高情绪识别的准确性和鲁棒性。实验结果表明，RDA-MTE模型在FER-2013数据集上的准确率达到83.54%，在CK+数据集上的准确率达到88.9%，尤其擅长识别“快乐”和“惊讶”等积极情绪。此外，该模型在消融实验中表现出很强的稳定性，验证了其在不同情绪类别中的可靠性和泛化能力。本研究不仅扩展了情感计算和运动行为决策领域的研究方法，还为实际应用中情绪识别系统的开发提供了重要参考。本研究的结果将增进对情绪在运动行为中作用的理解，并推动相关领域的发展。
