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[基于机器学习的轨迹预测建模方法用于手动针操纵]
1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Zhongguo zhen jiu = Chinese acupuncture & moxibustion
|September 15, 2025
概括
本研究介绍了一种机器学习模型,用于预测手动针操纵 (MAM) 轨迹. 该模型增强了针的精度和一致性,有助于技能传递和错误纠正.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 针研究 针研究
背景情况:
- 手动针操纵 (MAM) 需要高精度和一致性.
- 目前在MAM中培训和纠错的方法可能是主观的.
- 技术进步为标准化和改进针技术提供了机会.
研究的目的:
- 开发一种基于机器学习的方法,用于预测手动针操纵 (MAM) 过程中的轨迹.
- 为了提高针医生的操作的精度和一致性.
- 为MAM错误纠正提供实时建议,并促进技能传输.
主要方法:
- 利用计算机视觉分析针针头持有期间的手部微动.
- 开发了一个3D坐标描述的手持手的食指关节.
- 设计了一种基于机器学习的MAM轨迹预测模型,专注于4种典型的MAM运动,整合关节角度和骨信息.
主要成果:
- 基于网络的长短期内存 (LSTM) MAM轨迹预测模型实现了最高的稳定性和精度,达到高达98%.
- 该预测模型在应用到针操纵技能传递时,证明了改进的学习效果.
- 分层随机对照试验验证了模型在技能传递中的作用.
结论:
- 基于机器学习的MAM预测模型为从业者提供精确的行动预测和反.
- 这项技术对于手动针手术的继承和错误校正非常有价值.
- 该研究强调了人工智能在标准化和推进针实践方面的潜力.
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