Espinilla, MacarenaDe-La-Hoz-Franco, EmiroBernal Monroy, Edna RocioAriza-Colpas, PaolaMendoza-Palechor, Fabio2020-12-242020-12-242021-01-05978-0-9981331-4-0http://hdl.handle.net/10125/70848The 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 problem10 pagesEnglishAttribution-NonCommercial-NoDerivatives 4.0 InternationalSoft Computing: Theory Innovations and Problem Solving Benefitsactivity recognitiondata-driven approachesdatasetsmart devicessmart environments10.24251/HICSS.2021.236