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UJA Human Activity Recognition multi-occupancy dataset

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Title:UJA Human Activity Recognition multi-occupancy dataset
Authors:Espinilla, Macarena
De-La-Hoz-Franco, Emiro
Bernal Monroy, Edna Rocio
Ariza-Colpas, Paola
Mendoza-Palechor, Fabio
Keywords:Soft Computing: Theory Innovations and Problem Solving Benefits
activity recognition
data-driven approaches
dataset
smart devices
show 1 moresmart environments
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Date Issued:05 Jan 2021
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
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70848
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.236
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Soft Computing: Theory Innovations and Problem Solving Benefits


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