  Human-robot collaborative assembly systems enhance the efficiency and
productivity of the workplace but may increase the workers' cognitive demand.
This paper proposes an online and quantitative framework to assess the
cognitive workload induced by the interaction with a co-worker, either a human
operator or an industrial collaborative robot with different control
strategies. The approach monitors the operator's attention distribution and
upper-body kinematics benefiting from the input images of a low-cost stereo
camera and cutting-edge artificial intelligence algorithms (i.e. head pose
estimation and skeleton tracking). Three experimental scenarios with variations
in workstation features and interaction modalities were designed to test the
performance of our online method against state-of-the-art offline measurements.
Results proved that our vision-based cognitive load assessment has the
potential to be integrated into the new generation of collaborative robotic
technologies. The latter would enable human cognitive state monitoring and
robot control strategy adaptation for improving human comfort, ergonomics, and
trust in automation.
