Maitre Julien, Bouchard Kévin et Gaboury Sébastien. (2020). Classification models for data fusion in human activity recognition : alternative architectures. Dans Catia Prandi et Johann Marquez-Barja (dir.), GoodTechs '20 : Proceedings of the 6th EAI International conference on smart objects and technologies for social good. (p. 72-77). New York, NY, United States : Association for computing machinery.
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URL officielle: http://dx.doi.org/doi:10.1145/3411170.3411233
Résumé
In this paper, we present alternative deep learning architectures that perform data fusion, more specifically, feature-level fusion in the context of human activity recognition. The proposed architectures combine statistical features from the time-domain and features extracted automatically with stacked convolutional layers. The power of these architectures relies on the fact that they can be fed by various sources of data (e.g., time series, images). Additionally, we exploited the publicly available Mobile Health dataset to assess the performances of the proposed architectures. The results obtained show that the architectures are suitable for future use.
| Type de document: | Chapitre de livre |
|---|---|
| Date: | 2020 |
| Lieu de publication: | New York, NY, United States |
| Identifiant unique: | 10.1145/3411170.3411233 |
| Sujets: | Sciences naturelles et génie > Sciences mathématiques > Informatique |
| Département, module, service et unité de recherche: | Départements et modules > Département d'informatique et de mathématique |
| Éditeurs: | Prandi, Catia Marquez-Barja, Johann |
| Mots-clés: | deep learning, activity recognition, fusion, features, feature-level fusion |
| Déposé le: | 12 févr. 2021 14:23 |
|---|---|
| Dernière modification: | 12 févr. 2021 14:23 |
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