Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments
Joint Authors
Sansano-Sansano, Emilio
Belmonte-Fernández, Óscar
Montoliu, Raúl
Gascó-Compte, Arturo
Caballer-Miedes, Antonio
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-25
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Telecommunications Engineering
Abstract EN
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.
American Psychological Association (APA)
Sansano-Sansano, Emilio& Belmonte-Fernández, Óscar& Montoliu, Raúl& Gascó-Compte, Arturo& Caballer-Miedes, Antonio. 2020. Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1192405
Modern Language Association (MLA)
Sansano-Sansano, Emilio…[et al.]. Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments. Mobile Information Systems No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1192405
American Medical Association (AMA)
Sansano-Sansano, Emilio& Belmonte-Fernández, Óscar& Montoliu, Raúl& Gascó-Compte, Arturo& Caballer-Miedes, Antonio. Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1192405
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192405