Human-to-Human Knowledge Transfer using Functional Electrical Stimulation

Abstract

The concept of the knowledge transfer from a human expert to another non-expert human through technological interfaces, where a task can be learned by using brain-to-brain or body-to-body connections has great potential for future applications in which traditional verbal or visual communication channels are not available. In this paper, we present a novel approach of human-to-human knowledge transfer using a system based on functional electrical stimulation (FES). Using the proposed approach, hand-arm movements from a human teacher are recognized through an electromyogram signal classification algorithm. Using a master-slave approach, the movement signals are then translated into electrical stimulation signals and transmitted to a human learner using a functional electrical stimulation device. In the experiment conducted, we show how a human expert teaches seven learners a task that consists of associating hand-arm movements with visual stimuli presented to the learners. Furthermore, cognitive engagement was monitored during the learning process using an electroencephalogram (EEG) system. Experimental results show that four out of seven participants were able to learn the task with an accuracy over 80% and their cognitive engagement correlates to their performance.