Hargrove Levi J, Englehart Kevin, Hudgins Bernard
Institute of Biomedical Engineering, Department of Electrical and Computer Engineering, University of New Brunswick, P.O. Box 4400, Fredericton, NB E3B 5A3, Canada.
IEEE Trans Biomed Eng. 2007 May;54(5):847-53. doi: 10.1109/TBME.2006.889192.
The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement should have less muscular crosstalk allowing for more independent control sites. However, it remains unclear if this benefit outweighs the loss of the more global information contained in the surface MES. This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which use multi-channel intramuscular MES as inputs. An experiment was designed during which surface and intramuscular MES were collected simultaneously for 10 different classes of isometric contraction. There was no significant difference in classification accuracy as a result of using the intramuscular MES measurement technique when compared to the surface MES measurement technique. Impressive classification accuracy (97%) could be achieved by optimally selecting only three channels of surface MES.
多年来，表面肌电信号（MES）一直被用作动力假肢控制器的输入信号。由于最近的技术进步，有理由假设很快就会出现可植入的肌电传感器，这将使内部MES能够用作这些控制器的输入信号。内部MES测量应该具有较少的肌肉串扰，从而允许更多的独立控制部位。然而，目前尚不清楚这种优势是否超过了表面MES中包含的更全面信息的损失。本文比较了六种基于模式识别的肌电控制器的分类准确率，这些控制器使用多通道表面MES作为输入，与使用多通道肌内MES作为输入的相同控制器进行比较。设计了一个实验，在此期间同时收集表面和肌内MES，用于10种不同类别的等长收缩。与表面MES测量技术相比，使用肌内MES测量技术时分类准确率没有显著差异。通过仅优化选择三个表面MES通道，可实现令人印象深刻的分类准确率（97%）。
