Improved Motion Classification with an Integrated Multimodal Exoskeleton Interface

Human motion intention detection is an essential part of the control of upper-body exoskeletons. While surface electromyography (sEMG)-based systems may be able to provide anticipatory control, they typically require exact placement of the electrodes on the muscle bodies which limits the practical use and donning of the technology. In this study, we propose a novel physical interface for exoskeletons with integrated sEMG- and pressure sensors. The sensors are 3D-printed with flexible, conductive materials and allow multi-modal information to be obtained during operation. A K-Nearest Neighbours classifier is implemented in an off-line manner to detect reaching movements and lifting tasks that represent daily activities of industrial workers. The performance of the classifier is validated through repeated experiments and compared to a unimodal EMG-based classifier. The results indicate that excellent prediction performance can be obtained, even with a minimal amount of sEMG electrodes and without specific placement of the electrode.

Publication year:2021

Keywords:human-machine interface, Classification, exoskeletons, machine learning, Intention recognition, EMG, Wearable sensor

BOF-keylabel:yes

IOF-keylabel:yes

BOF-publication weight:1

Authors:Regional

Authors from:Government, Higher Education

Accessibility:Open