  Collaborative robotic systems will be a key enabling technology for current
and future industrial applications. The main aspect of such applications is to
guarantee safety for humans. To detect hazardous situations, current
commercially available robotic systems rely on direct physical contact to the
co-working person. To further advance this technology, there are multiple
efforts to develop predictive capabilities for such systems. Using motion
tracking sensors and pose estimation systems combined with adequate predictive
models, potential episodes of hazardous collisions between humans and robots
can be predicted. Based on the provided predictive information, the robotic
system can avoid physical contact by adjusting speed or position. A potential
approach for such systems is to perform human motion prediction with machine
learning methods like Artificial Neural Networks. In our approach, the motion
patterns of past seconds are used to predict future ones by applying a linear
Tensor-on-Tensor regression model, selected according to a similarity measure
between motion sequences obtained by Dynamic TimeWarping. For test and
validation of our proposed approach, industrial pseudo assembly tasks were
recorded with a motion capture system, providing unique traceable Cartesian
coordinates $(x, y, z)$ for each human joint. The prediction of repetitive
human motions associated with assembly tasks, whose data vary significantly in
length and have highly correlated variables, has been achieved in real time.
