Title: Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection
Authors: DelPreto, Joseph 
Salazar-Gomez, Andres F. 
Gil, Stephanie 
Hasani, Ramin  
Guenther, Frank H. 
Rus, Daniela 
Keywords: Human–robot interaction; EMG control; EEG control; Hybrid control structure; Plug-and-play supervisory control; Error-related potentials; Gesture detection; machine learning; control; human-machine interface; robotics
Issue Date: 9-Aug-2020
Journal: Autonomous Robots
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.
DOI: 10.1007/s10514-020-09916-x
Organisation: E191 - Institut für Computer Engineering 
License: CC BY 4.0 CC BY 4.0
Publication Type: Article
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