TY - JOUR
T1 - Toward Industry 5.0
T2 - A Neuroergonomic Workstation for a Human-Centered, Collaborative Robot-Supported Manual Assembly Process
AU - Knezevic, Nikola
AU - Savic, Andrej
AU - Gordic, Zavisa
AU - Ajoudani, Arash
AU - Jovanovic, Kosta
N1 - Publisher Copyright:
© 1994-2011 IEEE.
PY - 2025
Y1 - 2025
N2 - This article brings the concept of neuroergonomic workcell with its essential components (psychological and physical assessment, nonphysical, physical, and strategic support) for improving the well-being and productivity of workers at their workplaces. A proof-of-concept neuroergonomic human-centered workstation is demonstrated in a real factory environment for a typical industrial laborious task: assembly. The pilot workstation introduces a fully portable, noninvasive electroencephalogram (EEG)-based users' mental workload assessment, a nonobtrusive human-machine interface, illustrative graphical assembly guidelines, a collaborative robot assistant, and an intelligent task scheduler. The subjects' performance and workload were assessed using a NASA Task Load Index questionnaire, three EEG workload indices, hand gesture detection accuracy, the number of errors, and task duration. We identified a notable correlation between multiple EEG indices of workload and NASA score results. The new workstation boosts productivity with better performance and fewer errors on the assembly line while reducing mental demand. Its modular design ensures easy integration and adaptation into factory settings, optimizing manual assembly processes.
AB - This article brings the concept of neuroergonomic workcell with its essential components (psychological and physical assessment, nonphysical, physical, and strategic support) for improving the well-being and productivity of workers at their workplaces. A proof-of-concept neuroergonomic human-centered workstation is demonstrated in a real factory environment for a typical industrial laborious task: assembly. The pilot workstation introduces a fully portable, noninvasive electroencephalogram (EEG)-based users' mental workload assessment, a nonobtrusive human-machine interface, illustrative graphical assembly guidelines, a collaborative robot assistant, and an intelligent task scheduler. The subjects' performance and workload were assessed using a NASA Task Load Index questionnaire, three EEG workload indices, hand gesture detection accuracy, the number of errors, and task duration. We identified a notable correlation between multiple EEG indices of workload and NASA score results. The new workstation boosts productivity with better performance and fewer errors on the assembly line while reducing mental demand. Its modular design ensures easy integration and adaptation into factory settings, optimizing manual assembly processes.
UR - https://www.scopus.com/pages/publications/85210992551
U2 - 10.1109/MRA.2024.3487323
DO - 10.1109/MRA.2024.3487323
M3 - Article
AN - SCOPUS:85210992551
SN - 1070-9932
VL - 32
SP - 103
EP - 113
JO - IEEE Robotics and Automation Magazine
JF - IEEE Robotics and Automation Magazine
IS - 3
ER -