  The human gaze is an important cue to signal intention, attention,
distraction, and the regions of interest in the immediate surroundings. Gaze
tracking can transform how robots perceive, understand, and react to people,
enabling new modes of robot control, interaction, and collaboration. In this
paper, we use gaze tracking data from a rich dataset of human motion
(TH\"OR-MAGNI) to investigate the coordination between gaze direction and head
rotation of humans engaged in various indoor activities involving navigation,
interaction with objects, and collaboration with a mobile robot. In particular,
we study the spread and central bias of fixations in diverse activities and
examine the correlation between gaze direction and head rotation. We introduce
various human motion metrics to enhance the understanding of gaze behavior in
dynamic interactions. Finally, we apply semantic object labeling to decompose
the gaze distribution into activity-relevant regions.
