Improved Motion Classification With an Integrated Multimodal Exoskeleton Interface

Abstract

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.

Overview of 3D Printed Exoskeleton Materials and Opportunities for Their AI-Based Optimization

“…Multimodal information from flexible, conductive sensors is fed to a classifier (usually K-Nearest Neighbors-kNN), calibrated against an EMG-based unimodal classifier. This gives a very good prediction performance even with a minimal number of sEMG electrodes and without their precise placement [44]. 3D-printed exoskeletons can also be used as scaffolds to induce soft tissue and bone growth.…”