Nagata Kentaro, Magatani Kazushige, Yamada Masafumi
Kanagawa Rehabilitation Institute, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2583-6. doi: 10.1109/IEMBS.2009.5335340.
Surface electromyogram (SEMG) is one of the most important biological signal in which the human motion intention is directly reflected. Many systems use SEMG as a source of a control signal. (We call them "SEMG system"). In order to develop SEMG system, constructions of discriminant function and SEMG measurement placement are important factors for accurate recognition. But standard criterions for selection of discriminant function and SEMG measurement placement have not been clearly defined. Almost all of the conventional SEMG system has decided to select measurement placements of SEMG according to standard general anatomical structure of the human body and that mainly focused on signal processing method. However, SEMG measurement placement is also critical for recognition accuracy and evaluating the effect of SEMG measurement placement is important. In this study, we investigate the effect of SEMG measurement placement in hand motion recognition accuracy. We use a 96-channels matrix-type surface multielectrode and four channels are selected as the SEMG measurement placements from the channels that compose multielectrode. 5,000 configurations of SEMG measurement placements are generated by randomly selected number and each configuration is assessed by motion recognition accuracy (i.e. Monte Carlo method). In order to consider the influence of discriminant analysis, our system employs the linear discriminant analysis and nonlinear discriminant analysis. Each selected SEMG measurement placement is evaluated by those two types of discriminant analysis and the results are compared with each other. The experimental results show that motion recognition accuracy differs between these two analyses even if the same SEMG measurement placement is used. Not all optimal measurement placements for linear discriminant function suit for nonlinear discriminant function. The outcome of these investigations, the SEMG measurement placement should be taken into consideration and it suggests the necessity of evaluating the optimal measurement placement depending on a discernment analysis.
表面肌电图（SEMG）是最重要的生物信号之一，其中直接反映了人体运动意图。许多系统将SEMG用作控制信号源。（我们称它们为“SEMG系统”）。为了开发SEMG系统，判别函数的构建和SEMG测量位置是准确识别的重要因素。但是，判别函数选择和SEMG测量位置的标准准则尚未明确界定。几乎所有传统的SEMG系统都根据人体标准的一般解剖结构来决定SEMG的测量位置，并且主要侧重于信号处理方法。然而，SEMG测量位置对于识别精度也至关重要，评估SEMG测量位置的效果很重要。在本研究中，我们研究了SEMG测量位置对手部运动识别精度的影响。我们使用96通道矩阵型表面多电极，并从构成多电极的通道中选择四个通道作为SEMG测量位置。通过随机选择数字生成5000种SEMG测量位置配置，并通过运动识别精度（即蒙特卡罗方法）评估每种配置。为了考虑判别分析的影响，我们的系统采用线性判别分析和非线性判别分析。通过这两种判别分析对每个选定的SEMG测量位置进行评估，并将结果相互比较。实验结果表明，即使使用相同的SEMG测量位置，这两种分析之间的运动识别精度也有所不同。并非所有线性判别函数的最佳测量位置都适用于非线性判别函数。这些研究的结果表明，应考虑SEMG测量位置，并表明有必要根据判别分析评估最佳测量位置。
