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一个基于三轴态度传感器和计算机视觉的针操纵分类系统
Meng Zhu1, Da-Ming Liu2, Jian Pei3
1School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China. 13137089662@163.com.
Zhen ci yan jiu = Acupuncture research
|December 26, 2023
概括
这项研究结合了视觉和传感器技术,以准确地分类针操纵. 开发的系统量化了物理参数和动态手势,有助于继承针技术.
科学领域:
- 整合和补充医学是整合和补充医学.
- 生物医学工程 生物医学工程
- 针研究 针研究
背景情况:
- 针操纵技术传统上是通过观察和实践来学习的.
- 对这些技术的客观量化具有挑战性,阻碍了标准化和知识转移.
研究的目的:
- 开发一个准确识别和分类针操纵的系统.
- 将视觉和传感器数据结合起来,用于对针技术的定量分析.
主要方法:
- 使用三轴态度传感器来捕获针头操纵参数 (加速,速度,角度).
- 使用计算机视觉和混合3D卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型进行动态手势识别.
- 集成的时间域物理参数与时空特征用于分类.
主要成果:
- 实现了高分类准确度的提升-推 (95.56%的增强,93.33%的减少) 和扭转 (95.56%的增强,91.11%的减少) 技术.
- 与仅使用传感器的方法相比,在识别准确度方面显著改善.
- 识别了不同的物理参数模式,用于不同的操纵类型.
结论:
- 开发的系统可以对针操纵参数进行定量分析.
- 它为针技术的客观分类和数字继承提供了基础.
- 这种方法提高了针操纵的识别和分类准确性.
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