Bouchard Kévin, Fortin-Simard Dany, Lapalu Jérémy, Gaboury Sébastien, Bouzouane Abdenour et Bouchard Bruno. (2015). Unsupervised spatial data mining for smart homes. Dans Peng Cui, Jennifer Di, Charu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong et Xindong Wu (dir.), 2015 IEEE International Conference on Data Mining Workshop (ICDMW). (p. 1-8). Piscataway, New Jersey : Institute of Electrical and Electronics Engineers.
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URL officielle: http://dx.doi.org/doi:10.1109/ICDMW.2015.126
Résumé
This paper presents a novel unsupervised spatial data mining model especially adapted for activity recognition inside a smart home. The goal of our research is to have a scalable, simple to implement model that could enable to recognize the resident's Activities of Daily Living (ADLs). Our algorithm exploits ubiquitous sensors and passive RFID technology to achieve the learning and the recognition. The RFID is used to track all objects in the smart home in real-time. An algorithm then extracts qualitative spatial features from the positions dataset. Finally a clustering is performed with an adapted version of the Flocking algorithm. Our experimental results are very encouraging with a classification rate ranging from 85% to 93%.
| Type de document: | Chapitre de livre |
|---|---|
| Date: | 2015 |
| Lieu de publication: | Piscataway, New Jersey |
| Identifiant unique: | 10.1109/ICDMW.2015.126 |
| Sujets: | Sciences naturelles et génie > Sciences mathématiques > Informatique Sciences naturelles et génie > Sciences mathématiques > Statistiques Sciences de la santé |
| Département, module, service et unité de recherche: | Départements et modules > Département d'informatique et de mathématique |
| Éditeurs: | Cui, Peng Di, Jennifer Aggarwal, Charu Zhou, Zhi-Hua Tuzhilin, Alexander Xiong, Hui Wu, Xindong |
| Mots-clés: | smart home, clustering, spatial reasoning, RFID, gesture |
| Déposé le: | 17 févr. 2021 19:23 |
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| Dernière modification: | 17 févr. 2021 19:23 |
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