Multimodal measurement of the mental workload during an assembly and disassembly task.

Mental workload overload is a major cause of human error in industrial tasks such as maintenance. Human errors can compromise not only system safety but also lead to high social and economic costs, reduce equipment productivity, and cause incidents, accidents, and fatalities. To this day, we do not...

Descripción completa

Detalles Bibliográficos
Publicado en:Theoretical Issues in Ergonomics Science Vol. 27; no. 1; pp. 89 - 105
Autores principales: Berthon, Lorrys, Fleury, Sylvain, Bernard, Fabien, Paquin, Raphael, Richir, Simon
Formato: pictorial research tables/charts Journal Article
Publicado: Taylor & Francis Ltd Jan2026
Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Mental workload overload is a major cause of human error in industrial tasks such as maintenance. Human errors can compromise not only system safety but also lead to high social and economic costs, reduce equipment productivity, and cause incidents, accidents, and fatalities. To this day, we do not have an adequate assessment of mental workload in maintenance, which would help design maintenance processes more effectively by incorporating this crucial aspect. The objective of this study is to determine the ability of our indicators to measure mental workload during a disassembly and assembly task in a laboratory condition. Thirty-six participants performed a disassembly and assembly task under two different mental workload conditions. Subjective measures (NASA-TLX), performance metrics (number of errors), and cardiovascular data (heart rate, heart rate variability, and breathing rate) were analysed. We observed a higher number of errors and elevated NASA-TLX scores in the high mental workload condition. Regarding cardiovascular data, interesting trends in the temporal domain were observed despite mostly non-significant results. Although conducted in a laboratory, this multimodal mental workload measurement method is promising for diagnosing and understanding operators' cognitive behaviour, and deserves validation in real-world maintenance conditions.