基于人工智能卷积神经网络的拳击行为识别与体育心理学助理
1School of Science of Physical Culture and Sports, Kunsan University, Kunsan, 54150, Korea.
Scientific reports
|April 1, 2024
概括
这项研究将拳击者心理学与人工智能相结合,创建了一个先进的拳击动作识别模型. 新的BERT-ResNet方法通过考虑心理状态和身体行为来提高分类准确性.
科学领域:
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 了解拳击者心理对于表现至关重要.
- 现有的拳击动作识别模型缺乏心理整合.
研究的目的:
- 为了研究拳击手在比赛前的心理状态.
- 开发一个以人工智能为驱动的拳击动作分类和识别模型,结合心理数据.
主要方法:
- 心理维度 (焦虑,自信等) 的系统测量. 使用规模调查.
- 构建一个新的BERT-ResNet模型,将心理数据与行动特征融合在一起.
- 与传统模型相比,使用损失值,精度和F1分数进行性能评估.
主要成果:
- 拟议的BERT-ResNet模型显著优于传统模型.
- 在拳击行动分类中达到96.86%的高准确度.
- 证明了将心理见解集成到AI模型中的有效性.
结论:
- 该研究成功开发了一种用于拳击动作分类的AI模型,该模型考虑了拳击者的心理.
- 该模型为拳击中的心理训练和行动分析提供了强大的支持.
- 突出了心理学和深度学习之间的协同作用,以加强体育表现分析.
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