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在集体学习中使用卷积神经网络进行训练分类
Gi-Seung Bang1, Seung-Bo Park1
1Department of Software Convergence Engineering, Inha University, Incheon 22212, Republic of Korea.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究引入了使用卷积神经网络 (CNN) 和集体学习的实时运动姿势分类系统. 该系统在各种练习中实现了高精度,有助于个性化的健身和物理治疗.
科学领域:
- 计算机科学 计算机科学
- 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:
- 随着COVID-19的流行,人们对家庭炼解决方案的需求增加了.
- 准确的实时运动姿势分类对于有效的远程健身指导至关重要.
研究的目的:
- 开发一种新的实时运动姿势分类系统.
- 通过集体学习和CNN来提高分类准确性.
主要方法:
- 使用的MediaPipe用于人体关节坐标和角度提取.
- 使用卷积神经网络 (CNN) 进行模式识别.
- 实施了一种集体学习方法,将多个的预测结合起来.
主要成果:
- 在健身基本数据集上实现了高精度 (92.12%),精度 (91.62%),回忆 (91.64%) 和F1得分 (91.58%).
- 成功分类练习,包括手臂抬起,,和头部按压实时.
结论:
- 拟议的系统证明了有效的实时运动姿势分类.
- 潜在的应用包括个性化的健身建议和物理治疗服务.
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