Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems

Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor loca...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-06, Vol.21 (11), p.3912
Main Authors: Pedrollo, Guilherme, Konzen, Andréa Aparecida, de Morais, Wagner Ourique, Pignaton de Freitas, Edison
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
Communication
Emergency response
Entrances
Health care facilities
Humans
Indoor localization
Localization
Machine learning
Monitoring
Neural networks
Neural Networks, Computer
Neurons
Nodes
Power failures
Sensors
Statistical analysis
Systems stability
Telemedicine
Virtual sensor
Virtual sensors
Wireless networks
Wireless sensor network
Wireless sensor networks
Wireless Technology
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Summary:Young, older, frail, and disabled individuals can require some form of monitoring or assistance, mainly when critical situations occur, such as falling and wandering. Healthcare facilities are increasingly interested in e-health systems that can detect and respond to emergencies on time. Indoor localization is an essential function in such e-health systems, and it typically relies on wireless sensor networks (WSN) composed of fixed and mobile nodes. Nodes in the network can become permanently or momentarily unavailable due to, for example, power failures, being out of range, and wrong placement. Consequently, unavailable sensors not providing data can compromise the system's overall function. One approach to overcome the problem is to employ virtual sensors as replacements for unavailable sensors and generate synthetic but still realistic data. This paper investigated the viability of modelling and artificially reproducing the path of a monitored target tracked by a WSN with unavailable sensors. Particularly, the case with just a single sensor was explored. Based on the coordinates of the last measured positions by the unavailable node, a neural network was trained with 4 min of not very linear data to reproduce the behavior of a sensor that become unavailable for about 2 min. Such an approach provided reasonably successful results, especially for areas close to the room's entrances and exits, which are critical for the security monitoring of patients in healthcare facilities.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21113912