UJA Human activity recognition multi-occupancy dataset

Activity Recognition Systems - ARS are proposed 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 (University of Jaén Ambient Intelligence, Spain) Smart Lab. 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 an excellent tool for addressing multioccupancy in smart environments.

• Autores: De-La-Hoz-Franco, Emiro; Bernal Monroy, E.R; Ariza Colpas, P; Mendoza Palechor, F; Estevez, M.E.
• Tipo de recurso: Article of journal
• Fecha de publicación: 2021
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/8495
• Acceso en línea: https://hdl.handle.net/11323/8495; https://repositorio.cuc.edu.co/
• Palabra clave: Acceleration of the inhabitant; intelligent floor for location; proximity and binary-sensors
• Rights: openAccess
• License: CC0 1.0 Universal