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Download fileEEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration
journal contribution
posted on 2021-02-26, 15:26 authored by Achim Buerkle, Will Eaton, Niels LohseNiels Lohse, Tom Bamber, Pedro FerreiraPedro FerreiraConsumer 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 ManufacturingVolume
70Publisher
Elsevier BVVersion
- VoR (Version of Record)
Rights holder
© The authorsPublisher 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
2021-02-08Publication date
2021-02-24Copyright date
2021ISSN
0736-5845Publisher version
Language
- en