Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection
Name
10514_2020_Article_9916.pdf
Size
3.99 MB
Format
Adobe PDF
Checksum (MD5)
0deba3701ada4dc84f6230e6ce284624
Author(s)
DelPreto, Joseph Jeff
Salazar Gomez, Andres Felipe
Gil, Stephanie
Hasani, Ramin
Guenther, Frank H
Rus, Daniela L
Date Issued
August 2020
Journal
Autonomous Robots
Publisher
Springer US
Citation
DelPreto, Joseph et al. "Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection." Autonomous Robots (August 2020): 1303–1322 © 2020 The Author(s)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10514-020-09916-x
