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UJA Human Activity Recognition multi-occupancy dataset

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dc.contributor.author Espinilla, Macarena
dc.contributor.author De-La-Hoz-Franco, Emiro
dc.contributor.author Bernal Monroy, Edna Rocio
dc.contributor.author Ariza-Colpas, Paola
dc.contributor.author Mendoza-Palechor, Fabio
dc.date.accessioned 2020-12-24T19:23:04Z
dc.date.available 2020-12-24T19:23:04Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70848
dc.description.abstract The main purpose of Activity Recognition Systems - ARS is to improve the quality of human life. An ARS uses predictive models to identify the activities that individuals are performing in different environments. Under data-driven approaches, these models are trained and tested in experimental environments from datasets that contain data collected from heterogeneous information sources. When several people interact (multi-occupation) in the environment from which data are collected, identifying the activities performed by each individual in a time window is not a trivial task. In addition, there is a lack of datasets generated from different data sources, which allow systems to be evaluated both from an individual and collective perspective. This paper presents the SaMO – UJA dataset, which contains Single and Multi-Occupancy activities collected in the UJAmI Smart Lab of the University of Jaén (Spain). The main contribution of this work is the presentation of a dataset that includes a new generation of sensors as a source of information (acceleration of the inhabitant, intelligent floor for location, proximity and binary-sensors) to provide a new point of view on the multi-occupancy problem
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Soft Computing: Theory Innovations and Problem Solving Benefits
dc.subject activity recognition
dc.subject data-driven approaches
dc.subject dataset
dc.subject smart devices
dc.subject smart environments
dc.title UJA Human Activity Recognition multi-occupancy dataset
dc.identifier.doi 10.24251/HICSS.2021.236
prism.startingpage 1938
Appears in Collections: Soft Computing: Theory Innovations and Problem Solving Benefits


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