  We study sensor-based human activity recognition in manual work processes
like assembly tasks. In such processes, the system states often have a rich
structure, involving object properties and relations. Thus, estimating the
hidden system state from sensor observations by recursive Bayesian filtering
can be very challenging, due to the combinatorial explosion in the number of
system states. To alleviate this problem, we propose an efficient Bayesian
filtering model for such processes. In our approach, system states are
represented by multi-hypergraphs, and the system dynamics is modeled by graph
rewriting rules. We show a preliminary concept that allows to represent
distributions over multi-hypergraphs more compactly than by full enumeration,
and present an inference algorithm that works directly on this compact
representation. We demonstrate the applicability of the algorithm on a real
dataset.
