  Attention (and distraction) recognition is a key factor in improving
human-robot collaboration. We present an assembly scenario where a human
operator and a cobot collaborate equally to piece together a gearbox. The setup
provides multiple opportunities for the cobot to adapt its behavior depending
on the operator's attention, which can improve the collaboration experience and
reduce psychological strain. As a first step, we recognize the areas in the
workspace that the human operator is paying attention to, and consequently,
detect when the operator is distracted. We propose a novel deep-learning
approach to develop an attention recognition model. First, we train a
convolutional neural network to estimate the gaze direction using a publicly
available image dataset. Then, we use transfer learning with a small dataset to
map the gaze direction onto pre-defined areas of interest. Models trained using
this approach performed very well in leave-one-subject-out evaluation on the
small dataset. We performed an additional validation of our models using the
video snippets collected from participants working as an operator in the
presented assembly scenario. Although the recall for the Distracted class was
lower in this case, the models performed well in recognizing the areas the
operator paid attention to. To the best of our knowledge, this is the first
work that validated an attention recognition model using data from a setting
that mimics industrial human-robot collaboration. Our findings highlight the
need for validation of attention recognition solutions in such full-fledged,
non-guided scenarios.
