  Reasoning on the context of human beings is crucial for many real-world
applications especially for those deploying autonomous systems (e.g. robots).
In this paper, we present a new approach for context reasoning to further
advance the field of human motion prediction. We therefore propose a
neuro-symbolic approach for human motion prediction (NeuroSyM), which weights
differently the interactions in the neighbourhood by leveraging an intuitive
technique for spatial representation called Qualitative Trajectory Calculus
(QTC). The proposed approach is experimentally tested on medium and long term
time horizons using two architectures from the state of art, one of which is a
baseline for human motion prediction and the other is a baseline for generic
multivariate time-series prediction. Six datasets of challenging crowded
scenarios, collected from both fixed and mobile cameras, were used for testing.
Experimental results show that the NeuroSyM approach outperforms in most cases
the baseline architectures in terms of prediction accuracy.
