Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection
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Published version
Date
2020-09
Authors
DelPreto, Joseph
Salazar-Gomez, Andres F.
Gil, Stephanie
Hasani, Ramin
Guenther, Frank H.
Rus, Daniela
Version
Published version
OA Version
Citation
Joseph DelPreto, Andres F Salazar-Gomez, Stephanie Gil, Ramin Hasani, Frank H Guenther, Daniela Rus. 2020. "Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection." Autonomous Robots, Volume 44, Issue 7, pp. 1303 - 1322. https://doi.org/10.1007/s10514-020-09916-x
Abstract
Effective human supervision of robots can be key for ensuring correct robot operation in a variety of potentially safety-critical scenarios. This paper takes a step towards fast and reliable human intervention in supervisory control tasks by combining two streams of human biosignals: muscle and brain activity acquired via EMG and EEG, respectively. It presents continuous classification of left and right hand-gestures using muscle signals, time-locked classification of error-related potentials using brain signals (unconsciously produced when observing an error), and a framework that combines these pipelines to detect and correct robot mistakes during multiple-choice tasks. The resulting hybrid system is evaluated in a “plug-and-play” fashion with 7 untrained subjects supervising an autonomous robot performing a target selection task. Offline analysis further explores the EMG classification performance, and investigates methods to select subsets of training data that may facilitate generalizable plug-and-play classifiers.
Description
License
Copyright © 2020, The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.