Learning and adaptation of a Stylistic Myoelectric Interface: EMG-based robotic control with individual user differences

Abstract

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.