Learning and adaptation of a Stylistic Myoelectric Interface: EMG-based robotic control with individual user differences
Learning and adaptation of a Stylistic Myoelectric Interface: EMG-based robotic control with individual user differences
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DOI：
10.1109/robio.2011.6181317
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Takamitsu Matsubara;S. Hyon;J. Morimoto
中科院分区：
文献类型：
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作者：
Takamitsu Matsubara;S. Hyon;J. Morimoto
在这项研究中，我们提出了一种界面，通过使用从人类用户检测到的肌电信号来直观地控制机器人设备。特别是，我们展示了使用从前臂测量的肌电信号来控制机械手的实验。当用户执行不同的动作(例如，抓握和捏合)时，测量不同的肌电信号。另一方面，当不同的用户执行相同的动作(例如，抓握)时，也会测量不同的肌电信号。因此，设计一种可供不同用户执行不同动作的肌电接口是困难的。在这项研究中，我们提出了一个双线性模型来解释依赖于用户和运动的肌电信号。双线性模型由两个线性因素组成：1)用户相关因素和2)运动相关因素。由于运动依赖因子可以被解释为多个用户之间的运动的公共表示，因此它允许构建通常适用于多个用户到机器人设备的肌电接口。我们给出了使用从多个受试者采集的一组肌电信号来学习模型的过程，并给出了仅通过几次交互就使模型适应新用户的自适应过程。我们将模型和适应过程的结合称为风格肌电界面。通过实验，将该接口应用于基于肌电的机械手控制，验证了该接口在多用户操作中的有效性。
In this study, we propose an interface to intuitively control robotic devices by using myoelectric signals detected from human users. In particular, we show experiments in which myoelectric signals measured from a forearm are used to control a robotic hand. When a user performs different motions (e.g., grasping and pinching), different myoelectric signals are measured. On the other hand, when different users perform the same motion (e.g, grasping), also, different myoelectric signals are measured. Therefore, designing a myoelectric interface that can be used for different users to perform different motions is difficult. In this study, we propose a bilinear model to explain myoelectric signals that depend on users and motions. The bilinear model is composed of two linear factors: 1) the user-dependent factor and 2) the motion-dependent factor. Since the motion-dependent factor can be interpreted as a common representation of motion among multiple users, it allows to construct a myoelectric interface that is commonly applicable to multiple users to robotic devices. We present a learning procedure for the model using a set of myoelectric signals captured from multiple subjects, and present an adaptation procedure that adapts the model to a new user through only a few interactions. We call the combination of the model and the adaptation procedure the Stylistic Myoelectric Interface. Through experiments, the interface was applied to EMG-based robotic hand control and its effectiveness for multiple users was demonstrated.