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Discovery of topological relations for spatial activity recognition

Bouchard Kévin, Bouzouane Abdenour et Bouchard Bruno. (2013). Discovery of topological relations for spatial activity recognition. Dans 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). (p. 73-80). Piscataway, N.J. : IEEE.

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URL officielle: http://dx.doi.org/doi:10.1109/CIDM.2013.6597220

Résumé

Human Activity Recognition (HAR) is a challenging problem that could enable an outstanding number of applications in pervasive computing. Many approaches have been developed to overcome this issue, but they all suffer from major drawbacks. While some use invasive sensors such as video-cameras and wearable technology, other exploit complex models to only recognize coarse-grained activities. In this paper, we propose to exploit the largely neglected spatial aspects in the smart home to recognize the activity of daily living (ADLs) of a resident in a noninvasive fashion. To do so, we designed an extension to well-known data mining algorithms that we exploit to automatically learn the models of the resident ADLs. The models are built from the retrieval of spatial patterns corresponding to the topological relationships of the smart home entities. We demonstrate the advantages of our new semi-supervised system through comprehensive experiments inside a smart home and compare the results with expert defined models of activity.

Type de document:Chapitre de livre
Date:2013
Lieu de publication:Piscataway, N.J.
Identifiant unique:10.1109/CIDM.2013.6597220
Sujets:Sciences naturelles et génie > Sciences mathématiques > Informatique
Sciences de la santé
Département, module, service et unité de recherche:Départements et modules > Département d'informatique et de mathématique
Mots-clés:spatial data mining, smart home, topology, activity recognition, Smart homes, hidden Markov models, data mining, spatial databases, sensors, knowledge based systems, probabilistic logic, proceedings
Déposé le:12 févr. 2021 20:36
Dernière modification:12 févr. 2021 20:36
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