Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric Interface
Bilinear Modeling of EMG Signals to Extract User-Independent Features for Multiuser Myoelectric Interface
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DOI：
10.1109/tbme.2013.2250502
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发表时间：
2013-08-01
影响因子：
4.6
通讯作者：
Morimoto, Jun
中科院分区：
文献类型：
--
作者：
Matsubara, Takamitsu;Morimoto, Jun
在这项研究中，我们提出了一个多用户肌电接口，可以很容易地适应新的用户。当用户执行不同的运动（例如，抓握和夹捏），测量不同的肌电图（EMG）信号。当不同的用户执行相同的运动时（例如，抓握）时，还测量不同的EMG信号。因此，设计可由多个用户使用以执行多个运动的肌电接口是困难的。为了科普这个问题，我们针对肌电信号提出了一个双线性模型，该模型由两个线性因子组成：1）用户相关和2）运动相关。通过将肌电信号分解为这两个因子，提取的运动相关因子可以作为用户无关的特征。我们可以在提取的特征空间上构造一个运动分类器来开发多用户界面。对于新用户，所提出的自适应方法估计用户依赖的因素，通过只有几个交互。估计用户相关因子的双线性EMG模型可以从新的用户数据中提取与用户无关的特征。我们应用我们提出的方法识别任务的五个手势，机器人手控制使用四通道肌电信号测量从受试者前臂。我们的方法导致73%的准确性，这是统计学上显着不同的标准非多用户界面的准确性，作为一个双样本t检验的结果，在1%的显着性水平。
In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t-test at a significance level of 1%.