Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments
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Otros documentos de la autoría: Sansano-Sansano, Emilio; Belmonte-Fernández, Óscar; Montoliu Colás, Raul; Gascó Compte, Arturo; Caballer Miedes, Antonio
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
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comunitat-uji-handle3:10234/8619
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INVESTIGACIONMetadatos
Título
Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living EnvironmentsAutoría
Fecha de publicación
2020Editor
HindawiISSN
1574-017X; 1875-905XCita bibliográfica
SANSANO-SANSANO, Emilio, et al. Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments. Mobile Information Systems, 2020, vol. 2020.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.hindawi.com/journals/misy/2020/5204158/Versión
info:eu-repo/semantics/publishedVersionResumen
A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal ... [+]
A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies. [-]
Publicado en
Mobile Information Systems, 2020, vol. 2020.Proyecto de investigación
RTI2018-095168-B-C53, UJI-B2017-45, PRX18/00123Derechos de acceso
info:eu-repo/semantics/openAccess
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