  The ongoing trend towards Industry 4.0 has revolutionised ordinary
workplaces, profoundly changing the role played by humans in the production
chain. Research on ergonomics in industrial settings mainly focuses on reducing
the operator's physical fatigue and discomfort to improve throughput and avoid
safety hazards. However, as the production complexity increases, the cognitive
resources demand and mental workload could compromise the operator's
performance and the efficiency of the shop floor workplace. State-of-the-art
methods in cognitive science work offline and/or involve bulky equipment hardly
deployable in industrial settings. This paper presents a novel method for
online assessment of cognitive load in manufacturing, primarily assembly, by
detecting patterns in human motion directly from the input images of a stereo
camera. Head pose estimation and skeleton tracking are exploited to investigate
the workers' attention and assess hyperactivity and unforeseen movements. Pilot
experiments suggest that our factor assessment tool provides significant
insights into workers' mental workload, even confirmed by correlations with
physiological and performance measurements. According to data gathered in this
study, a vision-based cognitive load assessment has the potential to be
integrated into the development of mechatronic systems for improving cognitive
ergonomics in manufacturing.
