Mapping factory workers' characteristics with emotional states and task performance: a case study in asset maintenance

This dataset originates from a laboratory-controlled case study investigating the impact of operator characteristics, emotional states, and task conditions on performance during maintenance activities. It includes data from 22 participants (each completing two trials under varying workload conditions), capturing demographic variables (age, gender, expertise level), task design factors (task complexity and time pressure), and emotional responses using the Self-Assessment Manikin (SAM) for arousal and valence. Objective performance metrics such as task completion time, number of attempts, errors, and success in resolving anomalies are recorded, alongside subjective assessments of task appropriateness relative to participants’ expertise. The dataset supports research on human factors, emotional regulation, and cognitive workload in industrial and maintenance settings.