Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract— This paper discusses a navigation of an electric wheelchair (EWC) using bio-signals. As bio-signals, electromyogram (EMG), electrooculogram (EOG) and electroencephalogram (EEG) are considered. In navigation of the EWC using EMG, by taking degree and duration of the muscular activity into consideration for navigation of the EWC, travel time of the EWC was reduced. This enhances the maneuverability of the EWC. In navigation of the EWC using EOG, voluntary blink and involuntary blink were distinguished by using support vector machine (SVM). As a result, the average distinction accuracy for voluntary blink of 98.9% was obtained. This implies that the safety to stop the EWC is enhanced. In navigation of the EWC using EEG, operator's EEGs of concentrated state and of relaxed state were distinguished using the SVM. Then, forward moving of the EWC when the operator's EEG of concentrated state was recognized, was successfully achieved. In addition, navigation method of the EWC based on Steady State Visual Evoked Potentials (SSVEP) was proposed.
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