一种基于深度学习的方法,用于对体育舞蹈的情感分析
1Department of Physical Education, Qiannan Normal College for Nationalities, Dunyun, Guizhou, China.
PeerJ. Computer science
|July 6, 2023
概括
这项研究引入了一种新的体育舞蹈 (SP) 训练方法,将情绪识别与技术运动分析相结合. 开发的模型准确地识别了关键姿势,并对表演者的情绪进行了分类,提高了训练的有效性.
科学领域:
- 运动科学 运动科学 运动科学
- 情感计算是一种情感计算.
- 人工智能的人工智能
背景情况:
- 体育舞蹈 (SP) 训练往往优先考虑技术而不是情感,阻碍了运动情感融合和训练结果.
- 在SP表演者中有效的情绪识别对于整体训练和性能提升至关重要.
研究的目的:
- 通过分析运动舞蹈表演者的技术动作,开发和验证一种用于识别运动舞蹈表演者的情绪的新型模型.
- 通过先进的计算方法来解决体育舞蹈训练中忽视情感表达的问题.
主要方法:
- 使用Kinect 3D传感器捕获视频数据并提取关键特征点以进行姿势估计.
- 采用了一个融合神经网络模型 (FUSNN),与激发 - 价值 (AV) 情感模型集成.
- 修改了FUSNN,将LSTM替换为GRU,结合层规范化和层掉落,并减少情绪分类的堆级别.
主要成果:
- 拟议的模型准确地检测了体育舞蹈技术动作中的关键特征点.
- 获得了高的情感识别准确度:72.3%的4类和47.8%的8类.
- 证明了模型在精确的姿势检测和有效的情绪分类中对SP表演者的能力.
结论:
- 开发的模型显著促进了体育舞蹈表演者的情感识别和缓解.
- 将情绪识别集成到体育舞蹈训练中可以增强整体训练效果和表演者的幸福感.
- 这项研究为更具情感意识和更有效的体育舞蹈训练方法提供了基础.
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