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Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs
Felske, Timon
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Lüdtke, Stefan
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Bader, Sebastian
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Kirste, Thomas
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Dokumenttyp:
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Präsentation auf Konferenz
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Erscheinungsjahr:
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2022
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Veranstaltungstitel:
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2nd Workshop on Graphs and more Complex Structures for Learning and Reasoning ; colocated with AAAI 2022
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Veranstaltungsort:
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online
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Veranstaltungsdatum:
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28.02.2022
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Verwandte URLs:
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
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Fachgebiet:
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004 Informatik
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Abstract:
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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.
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