基于深度学习和人工智能的初中生适合体育游戏的应用
Xueyan Ji1, Shamsulariffin Bin Samsudin2, Muhammad Zarif Bin Hassan3
1Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
Scientific reports
|May 16, 2025
概括
本研究介绍了一种人工智能驱动的动作检测算法,用于初中体育教育. 它准确地评估学生的练习,如坐,提高教学质量和个性化发展.
科学领域:
- 教育技术的教育技术
- 计算机科学 计算机科学
- 运动科学 运动科学 运动科学
背景情况:
- 由于资源和效率的局限性,传统体育与个性化反和质量改进作斗争.
- 人工智能 (AI) 和深度学习为增强体育教育提供了创新的解决方案.
- 小学高中体育教育需要改进的方法来加强学生的身体和培养终身的体育习惯.
研究的目的:
- 使用MediaPipe框架开发一个人工智能驱动的空间时间图形卷积网络 (ST-GCN) 动作检测算法.
- 准确地识别和分析初中学生在体育活动中的表现,特别是坐.
- 提高体育教育的适应能力,提高教学质量,支持学生个性化的发展.
主要方法:
- 利用人工智能和深度学习,整合用于ST-GCN算法开发的MediaPipe框架.
- 使用态度估计技术来获取人类骨点数据.
- 构建了一个时空图模型,将骨架点表示为节点,它们的连接表示为边缘.
主要成果:
- 在使用ST-GCN算法对HMDB51数据集实现了88.3%的平均检测准确度.
- 与其他算法相比,在1000ms表现出优越的长期预测能力 (> 500ms),平均绝对误差 (71.1) 和平均每关节位置误差 (1.04) 显著降低.
- 在学生体育活动中,ST-GCN算法显著提高了动作识别准确度.
结论:
- 基于AI的ST-GCN动作检测算法提高了初中学生体育活动的准确性.
- 为体育教育提供即时,准确的反,帮助运动纠正和技能增强.
- 通过向教师提供对学生体能表现的更深入洞察力,支持差异化的教学.
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