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

arXiv: https://arxiv.org/abs/2202.00332 · HTML: https://mlsanity.com/paper/2202.00332/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Fine-grained activity recognition for assembly videos](https://mlsanity.com/paper/2012.01392/) (2020)
- [Lifted Filtering via Exchangeable Decomposition](https://mlsanity.com/paper/1801.10495/) (2018)

## Abstract (arXiv)

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.