Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes - IMT Atlantique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes

Résumé

Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it poses challenges in terms of variability of the environment, sensorimotor system, but also user habits. Therefore, endto-end systems fail at automatically extracting key features, without extensive pre-processing. We propose to tackle feature extraction for activity recognition in smart homes by merging methods from the Natural Language Processing (NLP) and the Time Series Classification (TSC) domains. We evaluate the performance of our method on two datasets issued from the Center for Advanced Studies in Adaptive Systems (CASAS). Moreover, we analyze the contributions of the use of NLP encoding Bag-Of-Word with Embedding as well as the ability of the FCN algorithm to automatically extract features and classify. The method we propose shows good performance in offline activity classification. Our analysis also shows that FCN is a suitable algorithm for smart home activity recognition and hightlights the advantages of automatic feature extraction.
Fichier principal
Vignette du fichier
samplepaper.pdf (301.08 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03032449 , version 1 (30-11-2020)

Identifiants

Citer

Damien Bouchabou, Sao Mai Nguyen, Christophe Lohr, Ioannis Kanellos, Benoit Leduc. Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes. DL-HAR 2021: 2nd International Workshop on Deep Learning for Human Activity Recognition, Jan 2021, Yokohama, Japan. pp.111-125, ⟨10.1007/978-981-16-0575-8_9⟩. ⟨hal-03032449⟩
228 Consultations
139 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More