Högskolan i Skövde

Indroduction: As the manufacturing assembly industry advances, increased customizations and product variety results in operators’ executing more cognitively complex tasks. To bridge these cognitive challenges, the assessment of operators’ health and performance in relation to their tasks has become an increasingly important topic in the field of cognitive ergonomics.

Methods: This paper examines operators’ mental workload through an integrated approach by implementing measures covering different mental workload signals: physiological, performance-based, and subjective, while assembling a 3D-printed drone. In this study, four validated mental workload instruments were used and their correlation levels were evaluated: error rate, completion time, the Rating Scale Mental Effort (RSME), and Heart Rate Variability (HRV).

Results: The results indicate that three out of four mental workload measures significantly correlate and can effectively be used to support the assessment of mental workload. More specifically, error rate, completion time, and RSME.

Discussion: Since current literature has stressed the importance of developing a multidimensional mental workload assessment framework, this paper contributes with new findings applicable to the manufacturing assembly industry.