相关实验视频
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A Novel Application of Musculoskeletal Ultrasound Imaging
Published on: September 17, 2013
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使用前臂超声波同时估计手部配置和手指关节角度
Keshav Bimbraw1, Christopher J Nycz2, Matthew Schueler1
1Department of Robotics Engineering, Worcester Polytechnic Institute, Worcester, MA 01609 USA.
IEEE transactions on medical robotics and bionics
|June 13, 2025
概括
这项研究引入了一种深度学习管道,使用前臂超声波图像来预测元手腕关节 (MCP) 关节角度,并为直观的人机交互分类手的配置.
科学领域:
- 机器人和人机交互的人机交互
- 生物医学工程 生物医学工程
- 机器学习和深度学习
背景情况:
- 计算机和机器人技术的进步需要直观的人机界面.
- 手的运动识别,包括手的配置分类和手腕关节 (MCP) 关节角度检测,对于与数字和物理系统的无交互至关重要.
- 虽然表面电肌图 (sEMG) 和其他方法存在,前臂超声波为手部运动分析提供非侵入性肌肉骨可视化.
研究的目的:
- 开发和评估一个深度学习管道,用于从前臂超声波图像中估计MCP关节角度.
- 用机器学习算法对超声波数据进行应用,对手的配置进行分类.
- 解决关于从前臂超声波同时估计手部配置和MCP关节角度的文献上的差距.
主要方法:
- 基于卷积神经网络 (CNN) 的深度学习管道被提议用于预测MCP关节角度.
- 超声波图像是从11个不同的手部配置的实验对象获得的.
- 使用支持矢量分类器 (SVC),多层感知子和拟议的CNN进行了对比. 运动捕捉数据为联合角度提供了地面真相.
主要成果:
- 拟议的CNN实现了平均分类准确度为82.7±9.7%的手配置.
- 具有不同内核的支持矢量分类器 (SVC) 也显示了超过80%的分类准确性.
- 对于预测的MCP关节角度,获得了7.35°±1.3°的平均根平均平方误差 (RMSE).
结论:
- 开发的基于CNN的管道有效地估计MCP关节角度,并从前臂超声图像分类手的配置.
- 该系统实现了低延迟 (6.25 - 9.1 Hz),使其适合实时控制应用.
- 这种方法为增强人机界面和机器人系统的直观控制提供了一个有希望的方法.
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