Predicting human behaviour with recurrent neural networks
| dc.contributor.author | Almeida, Aitor | |
| dc.contributor.author | Azkune Galparsoro, Gorka | |
| dc.date.accessioned | 2026-03-02T10:18:13Z | |
| dc.date.available | 2026-03-02T10:18:13Z | |
| dc.date.issued | 2018-02-20 | |
| dc.date.updated | 2026-03-02T10:18:13Z | |
| dc.description.abstract | As the average age of the urban population increases, cities must adapt to improve the quality of life of their citizens. The City4Age H2020 project is working on the early detection of the risks related to mild cognitive impairment and frailty and on providing meaningful interventions that prevent these risks. As part of the risk detection process, we have developed a multilevel conceptual model that describes the user behaviour using actions, activities, and intra- and inter-activity behaviour. Using this conceptual model, we have created a deep learning architecture based on long short-term memory networks (LSTMs) that models the inter-activity behaviour. The presented architecture offers a probabilistic model that allows us to predict the user's next actions and to identify anomalous user behaviours. | en |
| dc.description.sponsorship | European Commission under the City4Age Project Grant Agreement (No. 689731); with the support of the NVIDIA Corporation with the donation of the Titan X Pascal GPU | en |
| dc.identifier.citation | Almeida, A., & Azkune, G. (2018). Predicting human behaviour with recurrent neural networks. Applied Sciences (Switzerland), 8(2). https://doi.org/10.3390/APP8020305 | |
| dc.identifier.doi | 10.3390/APP8020305 | |
| dc.identifier.eissn | 2076-3417 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14454/5288 | |
| dc.language.iso | eng | |
| dc.publisher | MDPI AG | |
| dc.rights | © 2018 by the authors | |
| dc.subject.other | Long short-term memory networks | |
| dc.subject.other | Behavior modelling | |
| dc.subject.other | Intelligent environments | |
| dc.subject.other | Activity recognition | |
| dc.title | Predicting human behaviour with recurrent neural networks | en |
| dc.type | journal article | |
| dcterms.accessRights | open access | |
| oaire.citation.issue | 2 | |
| oaire.citation.title | Applied Sciences (Switzerland) | |
| oaire.citation.volume | 8 | |
| oaire.licenseCondition | https://creativecommons.org/licenses/by/4.0/ | |
| oaire.version | VoR |
Archivos
Bloque original
1 - 1 de 1
Cargando...
- Nombre:
- almeida_predicting_2018.pdf
- Tamaño:
- 842.48 KB
- Formato:
- Adobe Portable Document Format