UJA Human activity recognition multi-occupancy dataset
Artículo de revista
2021-06
Proceedings of the 54th Hawaii International Conference on System Sciences
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.
- Artículos científicos [3120]
Descripción:
UJA Human Activity Recognition multi-occupancy dataset.pdf
Título: UJA Human Activity Recognition multi-occupancy dataset.pdf
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Título: UJA Human Activity Recognition multi-occupancy dataset.pdf
Tamaño: 682.0Kb
PDFLEER EN FLIP
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