  Robots are increasingly present in our lives, sharing the workspace and tasks
with human co-workers. However, existing interfaces for human-robot interaction
/ cooperation (HRI/C) have limited levels of intuitiveness to use and safety is
a major concern when humans and robots share the same workspace. Many times,
this is due to the lack of a reliable estimation of the human pose in space
which is the primary input to calculate the human-robot minimum distance
(required for safety and collision avoidance) and HRI/C featuring machine
learning algorithms classifying human behaviours / gestures. Each sensor type
has its own characteristics resulting in problems such as occlusions (vision)
and drift (inertial) when used in an isolated fashion. In this paper, it is
proposed a combined system that merges the human tracking provided by a 3D
vision sensor with the pose estimation provided by a set of inertial
measurement units (IMUs) placed in human body limbs. The IMUs compensate the
gaps in occluded areas to have tracking continuity. To mitigate the lingering
effects of the IMU offset we propose a continuous online calculation of the
offset value. Experimental tests were designed to simulate human motion in a
human-robot collaborative environment where the robot moves away to avoid
unexpected collisions with de human. Results indicate that our approach is able
to capture the human\textsc's position, for example the forearm, with a
precision in the millimetre range and robustness to occlusions.
