  Ensuring the safety of human workers in a collaborative environment with
robots is of utmost importance. Although accurate pose prediction models can
help prevent collisions between human workers and robots, they are still
susceptible to critical errors. In this study, we propose a novel approach
called deep ensembles of temporal graph neural networks (DE-TGN) that not only
accurately forecast human motion but also provide a measure of prediction
uncertainty. By leveraging deep ensembles and employing stochastic Monte-Carlo
dropout sampling, we construct a volumetric field representing a range of
potential future human poses based on covariance ellipsoids. To validate our
framework, we conducted experiments using three motion capture datasets
including Human3.6M, and two human-robot interaction scenarios, achieving
state-of-the-art prediction error. Moreover, we discovered that deep ensembles
not only enable us to quantify uncertainty but also improve the accuracy of our
predictions.
