Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs

TL;DR

This paper introduces an efficient Bayesian filtering approach for activity recognition in assembly tasks, modeling complex system states with multi-hypergraphs and graph rewriting rules to handle combinatorial complexity.

Contribution

The paper presents a novel Bayesian filtering model using multi-hypergraphs and graph rewriting, enabling compact representation and inference for complex activity recognition tasks.

Findings

Effective recognition on real dataset

Compact representation reduces computational complexity

Applicable to complex assembly processes

Abstract

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.