Development of a Neuroergonomic Assessment for the Evaluation of Mental Workload in an Industrial Human–Robot Interaction Assembly Task: A Comparative Case Study

Author:

Caiazzo Carlo1,Savkovic Marija1ORCID,Pusica Milos23ORCID,Milojevic Djordje1,Leva Maria Chiara3ORCID,Djapan Marko1ORCID

Affiliation:

1. Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia

2. mBrainTrain d.o.o., Savska 19 G, 11000 Belgrade, Serbia

3. School of Environmental Health, Technological University Dublin, D07ADY7 Dublin, Ireland

Abstract

The disruptive deployment of collaborative robots, named cobots, in Industry 5.0 has brought attention to the safety and ergonomic aspects of industrial human–robot interaction (HRI) tasks. In particular, the study of the operator’s mental workload in HRI activities has been the research object of a new branch of ergonomics, called neuroergonomics, to improve the operator’s wellbeing and the efficiency of the system. This study shows the development of a combinative assessment for the evaluation of mental workload in a comparative analysis of two assembly task scenarios, without and with robot interaction. The evaluation of mental workload is achieved through a combination of subjective (NASA TLX) and real-time objective measurements. This latter measurement is found using an innovative electroencephalogram (EEG) device and the characterization of the cognitive workload through the brainwave power ratio β/α, defined after the pre-processing phase of EEG data. Finally, observational analyses are considered regarding the task performance of the two scenarios. The statistical analyses show how significantly the mental workload diminution and a higher level of performance, as the number of components assembled correctly by the participants, are achieved in the scenario with the robot.

Funder

European Commission, HORIZON 2020 Marie Sklodowska-Curie International Training Network Collaborative Intelligence for Safety Critical Systems, CISC

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering

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