[4F1] Body area sensing in human machine collaboration within industrial settings
R Haratian
Bournemouth University, UK
Collaboration of machines with humans within industrial settings could be prone to safety concerns due to the cognitive burden on the human during the collaboration in not being able to adapt to the machine functionality. Providing the capability for the machines to be able to recognise the user experience during the interaction with humans in order to be adaptive would address the safety concerns. The approach is based on monitoring the physiological signals of the users to recognise the user experience and the psychological condition during collaboration of humans and machines. It is achieved by incorporating recognised user experience in the machine decision-making process during the collaboration to adapt the functionality of the machine. As the psychological condition is reflected in physiological signals, sensing technologies and signal processing techniques to extract features from physiological signals are explored with applicability in the human machine collaboration scenario. The results show that the proposed approach has the potential to address safety in human machine collaboration within industrial settings.
