Human activity recognition using recurrent neural networks - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Human activity recognition using recurrent neural networks

Résumé

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.
Fichier principal
Vignette du fichier
456304_1_En_18_Chapter.pdf (212.36 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01629704 , version 1 (09-01-2018)

Licence

Paternité

Identifiants

Citer

Deepika Singh, Erinc Merdivan, Ismini Psychoula, Johannes Kropf, Sten Hanke, et al.. Human activity recognition using recurrent neural networks. 1st International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2017, Reggio, Italy. pp.267-274, ⟨10.1007/978-3-319-66808-6_18⟩. ⟨hal-01629704⟩
535 Consultations
273 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More