Bluetooth-Based System for Tracking People Localization at Home

Orozco-Ochoa, S.; Sobrino, Xosé Antón Vila; Rodriguez-Damian, M.; Rodríguez-Liñares, Leandro

doi:10.1007/978-3-642-19934-9_44

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The average percentage of correct classification is 91.875% for the 4-cross validations, as shown in Table 1. Following the method for the mean error calculation developed by us in previous work (Orozco-Ochoa et al, 2011) and based on the results from Table 2, an average error of 0.75 meter was obtained. The mean error is below other researchers' mean error, which is also around 2 meters, as has been shown in section 2.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, an important task would be the development of a system capable of detecting not only alarm conditions (falls or warnings from the user), but also anomalous behavior or cognitive impairments by continuously monitor the person at home. In order to do so, we address this issue in two ways: tracking with intelligent video analysis (Gómez-Conde et al, 2011) and localization via Bluetooth (Orozco-Ochoa et al, 2011). In the current paper, we present an algorithm which seeks to locate the "zone" where the user is.…”

Section: Introductionmentioning

confidence: 99%

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An Indoor Localization Algorithm Based in Weibull Distribution and Bayesian Classifier

Orozco-Ochoa

Sobrino²,

Gómez-Conde

et al. 2012

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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The location of objects and people by the use of Global Positioning System (GPS) or Global System for Mobile Communications (GSM) network is increasingly used to provide location-based services. These technologies work well outdoors and when the required accuracy is not very high, up to 10 meters. This paper describes an algorithm for monitoring elderly people at home, by continuously taking its position, which uses the RSS information exchanged between Bluetooth devices, Weibull distributions and Bayesian classifiers. This algorithm has been validated in a real environment, an area of 13 × 12 meters, with several rooms and corridors, where zones of approximately 6 square meters have been delimited. Our algorithm achieved a rate of correct detections of 91.875%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Indoor Localization Algorithm Based in Weibull Distribution and Bayesian Classifier

Orozco-Ochoa

Sobrino²,

Gómez-Conde

et al. 2012

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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show abstract

“…The papers in this area focus on system development or improvement and report on system accuracy and other performance criteria. In healthcare research, Bluetooth, wearable sensors and Wi-Fi are used to monitor people in assisted living situations (Orozco-Ochoa et al , 2011; Rodríguez-Martín et al , 2013; Villarrubia et al , 2014). The studies in this area report on technical aspects such as accuracy or system design.…”

Section: Introductionmentioning

confidence: 99%

Smart campus tools 2.0 exploring the use of real-time space use measurement at universities and organizations

Valks

Arkesteijn

Heijer

2019

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Purpose The purpose of this study is to generate knowledge about the use of smart campus tools to improve the effective and efficient use of campuses. Many universities are facing a challenge in attuning their accommodation to organisational demand. How can universities invest their resources as effectively as possible and not in space that will be poorly utilized? The hypothesis of this paper is that by using smart campus tools, this problem can be solved. Design/methodology/approach To answer the research question, previous survey at 13 Dutch universities was updated and compared with a survey of various universities and other organizations. The survey consisted of interviews with structured and semi-structured questions, which resulted in a unified output for 27 cases. Findings Based on the output of the cases, the development of smart campus tools at Dutch universities was compared to that of international universities and other organizations. Furthermore, the data collection led to insights regarding the reasons for initiating smart campus tools, user and management information, costs and benefits and foreseen developments. Originality/value Although the use of smart tools in practice has gained significant momentum in the past few years, research on the subject is still very technology-oriented and not well-connected to facility management and real estate management. This paper provides an overview of the ways in which universities and organizations are currently supporting their users, improving the use of their buildings and reducing their energy footprint through the use of smart tools.

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Indoor Tracking Persons Using Bluetooth: A Real Experiment with Different Fingerprinting-Based Algorithms

Rodriguez-Damian

Sobrino

Rodríguez-Liñares

2013

Ambient Intelligence - Software and Applications

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Bluetooth-Based System for Tracking People Localization at Home

Cited by 6 publications

References 9 publications

An Indoor Localization Algorithm Based in Weibull Distribution and Bayesian Classifier

An Indoor Localization Algorithm Based in Weibull Distribution and Bayesian Classifier

Smart campus tools 2.0 exploring the use of real-time space use measurement at universities and organizations

Indoor Tracking Persons Using Bluetooth: A Real Experiment with Different Fingerprinting-Based Algorithms

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