A Weightless Neural Network as a Classifier to translate EEG signals into Robotic hand commands

摘要

Automatic movement-prothesis control aims to increase the quality of life for patients with diseases causing temporary or permanent paralysis or, in the worst case, the lost of limbs. This technology requires the interaction between the user and the device through a control interface that detects the user's movement intention. Basing on the Motor-Imagery theory, many researchers have explored a wide variety of Classifiers to identify patients' physiological signals from many different sources in order to detect patients' moves intentions. We here propose a novel approach relying on the use of a Weightless Neural Network-based classifier, whose design lends itself to an easy hardware implementation. Additionally, we employ a non-invasive light weight and easy donning EEG-helmet in order to provide a portable controller interface. The developed interface is connected to a robotic hand for controlling open/close actions. We compared the proposed classifier with state of the art classifiers by showing that the proposed method achieves similar performance and contemporaneously represents a viable and practicable solution due to its portability on hardware devices, which will permit its direct implementation on the helmet board. In recent years, humanoid robots have become quite ubiquitous finding wide applicability in many different fields, spanning from education to entertainment and assistance. They can be considered as more complex cyber-physical systems (CPS) and, as such, they are exposed to the same vulnerabilities. This can be very dangerous for people acting that close with these robots, since attackers by exploiting their vulnerabilities, can not only violate people's privacy, but, more importantly, they can command the robot behavior causing them bodily harm, thus leading to devastating consequences. In this paper, we propose a solution not yet investigated in this field, which relies on the use of secure enclaves, which in our opinion could represent a valuable solution for coping with most of the possible attacks, while suggesting developers to adopt such a precaution during the robot design phase. Understanding emotional states is a challenging task which frequently leads to misinterpretation even in human observers. While the perception of emotions has been studied extensively in human psychology, little is known about what factors influence the human perception of emotions in robots and virtual characters. In this paper, we build on the Brunswik lens model to investigate the influence of (a) the agent's embodiment using a 2D virtual character, a 3D blended embodiment, a recording of the 3D platform and a recording of a human, as well as (b) the level of human-likeness on people's ability to interpret emotional facial expressions in an agent. In addition, we measure social traits of the human observers and analyze how they correlate to the success in recognizing emotional expressions. We find that interpersonal differences play a minor role in the perception of emotional states. However, both embodiment and human-likeness as well as related perceptual dimensions such as perceived social presence and uncanniness have an effect on the attribution of emotional states.