Evaluating Mental Workload and Productivity in Manufacturing: A Neuroergonomic Study of Human–Robot Collaboration Scenarios

The field of human–robot collaboration (HRC) still lacks research studies regarding the evaluation of mental workload (MWL) through objective measurement to assess the mental state of operators in assembly tasks. This research study presents a comparative neuroergonomic analysis to evaluate the mental workload and productivity in three laboratory experimental conditions: in the first, the participant assembles a component without the intervention of the robot (standard scenario); in the second scenario, the participant performs the same activity in collaboration with the robot (collaborative scenario); in the third scenario, the participant is fully guided in the task in collaboration with the robot (collaborative guided scenario) through a system of guiding labels according to Poka-Yoke principles. The assessment of participants’ mental workload is shown through combinative analysis of subjective (NASA TLX) and objective (electroencephalogram—EEG). Objective MWL was assessed as the power waves ratio β/α (Beta—stress indicator, Alpha—relaxation indicator). Furthermore, the research used observational measurements to calculate the productivity index in terms of accurately assembled components across the three scenarios. Through ANOVA RM, mental workload significantly decreased in the activities involving the cobot. Also, an increase in productivity was observed shifting from the manual scenario to the cobot-assisted one (18.4%), and to the collaborative guided scenarios supported by Poka-Yoke principles (33.87%).

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Publication Details

Journal
Machines
Published
2025-09-01
DOI
https://doi.org/10.3390/machines13090783
Citations
2
Primary Topic
Ergonomics and Human Factors
Type
article
Field-Weighted Citation Impact
1.35

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article

Evaluating Mental Workload and Productivity in Manufacturing: A Neuroergonomic Study of Human–Robot Collaboration Scenarios

Carlo Caiazzo, Marko Djapan, Marija Savković, Arso M. Vukićević et al.
2 citations
Machines
Ergonomics and Human Factors
1.35
article

Evaluating Mental Workload and Productivity in Manufacturing: A Neuroergonomic Study of Human–Robot Collaboration Scenarios

Carlo Caiazzo, Marko Djapan, Marija Savković, Arso M. Vukićević, Luca Gualtieri, Djordje Milojevic
article en
2 citations

Abstract

The field of human–robot collaboration (HRC) still lacks research studies regarding the evaluation of mental workload (MWL) through objective measurement to assess the mental state of operators in assembly tasks. This research study presents a comparative neuroergonomic analysis to evaluate the mental workload and productivity in three laboratory experimental conditions: in the first, the participant assembles a component without the intervention of the robot (standard scenario); in the second scenario, the participant performs the same activity in collaboration with the robot (collaborative scenario); in the third scenario, the participant is fully guided in the task in collaboration with the robot (collaborative guided scenario) through a system of guiding labels according to Poka-Yoke principles. The assessment of participants’ mental workload is shown through combinative analysis of subjective (NASA TLX) and objective (electroencephalogram—EEG). Objective MWL was assessed as the power waves ratio β/α (Beta—stress indicator, Alpha—relaxation indicator). Furthermore, the research used observational measurements to calculate the productivity index in terms of accurately assembled components across the three scenarios. Through ANOVA RM, mental workload significantly decreased in the activities involving the cobot. Also, an increase in productivity was observed shifting from the manual scenario to the cobot-assisted one (18.4%), and to the collaborative guided scenarios supported by Poka-Yoke principles (33.87%).

MachinesVol. 13(9)
University of Kragujevac (RS), Free University of Bozen-Bolzano (IT)
Canadian Institute of Steel Construction, European Commission
Openalex Percentile: Top 18%
Ergonomics and Human Factors
1.35
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