基于卷积神经网络的标准化运动检测和实时心率监测,用于基于卷积神经网络的有氧训练
1School of physical education, Guizhou University of Finance and Economics, Guiyang, Guizhou 550025, China.
Preventive medicine
|July 22, 2023
概括
卷积神经网络 (CNN) 通过精确监测学生的运动和心率来增强有氧训练. 这种人工智能方法标准化了体育教育,并改善了训练结果.
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
- 生物医学工程 生物医学工程
背景情况:
- 标准化有氧运动教学和训练需要科学监测运动和心率.
- 目前的方法在准确检测和监测方面面临挑战.
研究的目的:
- 提出基于卷积神经网络 (CNN) 的方法,以改善运动检测和心率监测在有氧运动中.
- 通过人工智能解决体育教育标准化的现有困难.
主要方法:
- 运动视频被加工成图像,背景被删除,视觉错误被纠正.
- CNNs用于图像预训练,以构建人体骨地图.
- CNN用于非接触式心率监测,整合时间数据并减少计算步骤.
主要成果:
- 实验结果表明,在检测学生运动时,准确度提高了.
- 在有氧训练期间,CNN有效监测心率变化.
- 拟议的方法提高了系统输出信号质量,并减少了计算资源的使用.
结论:
- 卷积神经网络为标准化有氧训练教学提供了一个可行的解决方案.
- 在体育教育中,CNN显著提高了运动和心率监测的准确性.
- 这种由人工智能驱动的方法优化了培训效率和有效性.
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