基于神经网络和人工智能的智能体操教学模型的构建
Guanxi Fan1, Yu Wang2, Tongling Wang3
1Department of Basic Courses, Guangzhou Institute of Science and Technology, Guangzhou, 510540, China.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种使用人工神经网络 (ANN),隐藏马尔科夫模型 (HMM) 和图形神经网络 (GNN) 的智能体操教学模型,用于准确的运动识别和个性化的反.
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
- 生物机械分析 生物机械分析
背景情况:
- 传统体操教学在量化进度和提供个性化指导方面面临挑战.
- 缺乏客观反会阻碍运动员获得技能和提高表现.
研究的目的:
- 开发一个智能体操教学模型,利用人工神经网络 (ANN),隐藏的马尔科夫模型 (HMM) 和图形神经网络 (GNN).
- 增强体操运动的识别和提供个性化,定量反,以改善教学和学习.
主要方法:
- 整合Kinect设备和多传感器数据以捕捉体操运动.
- 使用ANN进行特征提取 (联合角度,速度,加速).
- 应用HMM用于时间依赖性和GNN用于在运动序列中的复杂模式识别.
主要成果:
- 在UTKinectAction3D数据集上,ANN-HMM-GNN模型实现了高识别精度 (98.2%),回忆 (97.5%) 和F1得分 (97.8%).
- 在不同的照明条件下 (准确度>95%) 和适应不同运动员体格的适应性.
- 该模型提供了定量反,增强了体操教学的科学和个性化性质.
结论:
- 提出的智能模型显著改善了体操运动的识别,并提供了有效的,个性化的反.
- 这项技术为体操教育的智能发展提供了实质性的支持,优化了技能掌握和表现.
- 实时分析和反机制使教练和学习者能够进行更有效的培训.
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