scholarly journals An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

Sensors ◽  
2016 ◽  
Vol 17 (12) ◽  
pp. 36 ◽  
Author(s):  
Óscar Belmonte-Fernández ◽  
Adrian Puertas-Cabedo ◽  
Joaquín Torres-Sospedra ◽  
Raúl Montoliu-Colás ◽  
Sergi Trilles-Oliver
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Positioning System

scholarly journals Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Emilio Sansano-Sansano ◽  
Óscar Belmonte-Fernández ◽  
Raúl Montoliu ◽  
Arturo Gascó-Compte ◽  
Antonio Caballer-Miedes
Keyword(s):  
Assisted Living ◽  
Ambient Assisted Living ◽  
Indoor Positioning ◽  
Training Data ◽  
Sensor Data ◽  
Smart Devices ◽  
Positioning System ◽  
Positioning Systems ◽  
Future Performance ◽  
Data Scaling

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.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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