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基于sEMG的动作识别用于机器人手术培训 - 一项初步研究

Chenji Li, Chao Liu, Arnaud Huaulme

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    表面电肌图 (sEMG) 信号可以识别机器人手术训练中的手动. 这种肌肉活动分析为评估外科技能和指导学员改进提供了一个有前途的方法.

    科学领域:

    • 生物医学工程 生物医学工程
    • 机器人技术 机器人技术 机器人技术
    • 神经科学是一个神经科学.

    背景情况:

    • 机器人手术需要高效的外科医生培训和技能评估.
    • 传统方法可能无法充分解决机器人程序的学习曲线.
    • 表面电肌图 (sEMG) 提供了一种新的方法来分析训练期间的肌肉活动.

    研究的目的:

    • 探索在机器人手术培训中使用sEMG信号进行运动原始识别的可行性.
    • 从sEMG数据中开发可解释的信息,以提高学员的表现.
    • 为基于肌肉活动的新技能评估标准奠定基础.

    主要方法:

    • 利用机器学习 (ML) 技术来分析sEMG信号.
    • 专注于识别沿着3个笛卡尔轴的手动.
    • 采用商业机器人手术培训平台与虚拟现实 (VR) 环境.

    主要成果:

    • 展示了基于sEMG的手动识别的可行性和前景.
    • 识别了具有不同识别准确度的特定运动模式.
    • 建立sEMG作为技能评估和培训指导的潜在基础.

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    结论:

    • 基于sEMG的运动识别是机器人手术培训的可行工具.
    • 需要更多涉及深度学习的研究来提高所有运动模式的识别精度.
    • 这种方法可以带来更有效和客观的外科手术技能发展.