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纠正电肌图控制算法的标记训练数据中的时间不准确性.

Aaron T Wang, Connor D Olsen, W Caden Hamrick

    IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
    |November 9, 2023
    PubMed
    概括

    由于反应时间,电肌图 (EMG) 控制数据通常是错位的. 重整EMG数据可以提高离线性能,但在实时控制应用中无法提高在线性能.

    科学领域:

    • 生物医学工程 生物医学工程
    • 神经科学是一个神经科学.
    • 康复工程 康复工程

    背景情况:

    • 电肌图 (EMG) 控制系统使用监督学习来解释运动意图.
    • 培训数据质量对于EMG控制性能至关重要.
    • 目前收集EMG数据的方法假定完美同步,但由于固有的延迟,这种情况往往不太可能发生.

    研究的目的:

    • 量化EMG和动力学数据重新调整对分类和回归算法的影响.
    • 引入和评估一种新的试验逐试验重新调整方法.
    • 评估重新调整对线下和线上EMG控制性能的影响.

    主要方法:

    • 将全球交叉相关性重新调整与新的试验逐试验重新调整方法进行比较.
    • 评价了分类和回归算法的性能,有和没有人在循环中.
    • 分析了EMG和动力学数据,以发现固有的错位和反应时间不一致.

    主要成果:

    • 电磁波和动力学数据表现出固有的错位和不一致的反应时间.
    • 全球和逐试验的重新调整都显著改善了离线分类和回归性能.
    • 在离线分类中,试验逐试验的重新调整表现优于全球的重新调整.

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  • 没有观察到在线表现与或没有重新调整的显著差异.

    • 结论:

    • 标记的EMG数据含有不准确性,影响算法性能.
    • 重定位技术可以改善线下EMG控制模型培训.
    • 对在线性能进行重新调整的有效性需要对现实世界的EMG控制应用进行进一步调查.