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Creating and Exploring Semantic Annotation for Behaviour Analysis

Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation exten...

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Detalles Bibliográficos
Autores principales: Yordanova, Kristina, Krüger, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163329/
https://www.ncbi.nlm.nih.gov/pubmed/30142956
http://dx.doi.org/10.3390/s18092778
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author Yordanova, Kristina
Krüger, Frank
author_facet Yordanova, Kristina
Krüger, Frank
author_sort Yordanova, Kristina
collection PubMed
description Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen’s [Formula: see text] of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset.
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spelling pubmed-61633292018-10-10 Creating and Exploring Semantic Annotation for Behaviour Analysis Yordanova, Kristina Krüger, Frank Sensors (Basel) Article Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen’s [Formula: see text] of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset. MDPI 2018-08-23 /pmc/articles/PMC6163329/ /pubmed/30142956 http://dx.doi.org/10.3390/s18092778 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yordanova, Kristina
Krüger, Frank
Creating and Exploring Semantic Annotation for Behaviour Analysis
title Creating and Exploring Semantic Annotation for Behaviour Analysis
title_full Creating and Exploring Semantic Annotation for Behaviour Analysis
title_fullStr Creating and Exploring Semantic Annotation for Behaviour Analysis
title_full_unstemmed Creating and Exploring Semantic Annotation for Behaviour Analysis
title_short Creating and Exploring Semantic Annotation for Behaviour Analysis
title_sort creating and exploring semantic annotation for behaviour analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163329/
https://www.ncbi.nlm.nih.gov/pubmed/30142956
http://dx.doi.org/10.3390/s18092778
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