A co-bot quality cycle: neuroscientific view on fatigue and executive functions

Background: Industry 4.0 aims at developing collaborative robotic technology (co-bot) to improve efficiency, ergonomics and safety in the workplace. Aims: The aim of this contribution was to propose a quality-cycle to optimize human robot interaction (HRI) for co-bot technology and highlighting if the perceived cognitive effort and spatial perception change in HRI compared to human-to-human interactions. Methods: Based on the Deming quality-cycle and the concept of neuro-industrial engineering, we selected the following neuroscientific dimensions which should be integrated in the quality cycle for co-bot designing: fatigue, executive functions, attentional coordination, selective attention and space perception. Regarding the latter, we propose the consideration of peripersonal and extrapersonal dimensions. Results: The outcome is a four-phases cycle composed by the first step (planning) where a compartmentalization of the industrial processes is made and the brain-computer interface and neuroscientific methods are selected based on the type of HRI [e.g. to assess cognitive and emotional planning, (a/h)/b or (a/h)/(a?b) ratios in the frontal and central brain can be selected]. In the second phase (doing) virtual/real scenarios are executed while data is retrieved. In the third phase (modelling) data are used to create bottom-up models, which will be tested again in the future. Finally, in the last phase (change), evidence-based adjustments are implemented. Conclusion: An integrated perspective, which considers the worker from a holistic viewpoint, as the one presented, might make co-bots more human-oriented, leading to increased efficiency and safety by using data on human perception and spatial representation.