1-s2.0-S0736584521000223-main.pdf (4.19 MB)
Download file

EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration

Download (4.19 MB)
journal contribution
posted on 26.02.2021, 15:26 authored by Achim Buerkle, Will Eaton, Niels LohseNiels Lohse, Tom Bamber, Pedro FerreiraPedro Ferreira
Consumer markets demonstrate an observable trend towards mass customization. Assembly processes are required to adapt in order to meet the requirements of increased product complexity and constant variant updates. A concept to meet challenges within this trend, is a close collaboration between human workers and robots. Currently, in order to protect human operators, there are barriers and restrictions in place which prevent close collaboration. This is due to safety systems being mostly reactive, rather than anticipating motions or intentions. There are probabilistic models, which aim to overcome these limitations, yet predicting human behavior remains highly complex. Thus, it would be desirable to physically measure movement intentions in advance. A novel approach is presented of how upper-limb movement intentions can be measured with a mobile electroencephalogram (EEG). The human brain constantly analyses and evaluates motor movements up to 0.5 s before their execution. A safety system could therefore be enhanced to have an early warning of an upcoming movement. In order to classify the EEG-signals as fast as possible and to minimize fine-tuning efforts, a novel data processing methodology is introduced. This includes TimeSeriesKMeans labelling of movement intentions, which is then used to train a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The results suggested high detection accuracies and potential time gains of up to 513 ms to be achieved in a semi-online system. Thus, the time advantages included in a simulation demonstrated the potential to increase a system’s reaction time and therefore improve the safety and the fluency of Human-Robot Collaboration

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Robotics and Computer-Integrated Manufacturing

Volume

70

Publisher

Elsevier BV

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

08/02/2021

Publication date

2021-02-24

Copyright date

2021

ISSN

0736-5845

Language

en

Depositor

Dr Niels Lohse Deposit date: 26 February 2021

Article number

102137