A Collaborative Filtering Approach Toward Plug-and-Play Myoelectric Robot Control

Previous works in the literature have claimed that the characteristics of electromyography (EMG) signals depend on each person, and thus, EMG interfaces need to be carefully calibrated for each user in myoelectric control. In this study, we show that the EMG interface used to estimate the joint torques of a user can be constructed simply by incorporating other users’ data without typical calibration process. To achieve this plug-and-play capability, we introduce the concept of collaborative filtering to estimate the joint torque of a novel user by exploiting the preidentified relationships between motion-body features, including EMG signals, and the joint torques of other users. To validate our proposed approach, we compare the performance of estimating joint torque by the proposed method with that by conventional linear regression models as a baseline. We considered the following two baseline methods.
Linear-own:
The parameters of the linear model are calibrated for each subject from his/her own training data.
Linear-others:
The parameters of the linear model are calibrated with the other users’ data in which the novel user's data are not included. As a result, the estimated joint torques from our proposed approach reveal a better estimation performance than those from the baseline approaches. Furthermore, we also successfully demonstrate online myoelectric control of an upper limb exoskeleton robot with an attached mannequin arm.