BLE Beacon Based Patient Tracking in Smart Care Facilities

Kennedy, Brad; Taylor, Graham W.; Spachos, Petros

doi:10.1109/percomw.2018.8480315

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy [7].…”

Section: Related Workmentioning

confidence: 99%

Prototype of Beacon-Mobile Application for Medical Appointment Scheduling Management

Zea

Solano

Rondon

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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One of the frequent situations which arise in a health center, which creates many inconveniences to the users of health services, is the process of appointments to the various specialized issue. Although it has been designed and implemented many channels of care to obtain appointments, this is still a crucial problem; on the other hand, it is known that the use of mobile devices becomes more usually in the daily activities of the people. In this paper we propose the prototype of a mobile application that allows users a fast and autonomous way to obtain an appointment in the speciality that they require using external devices called Beacons with Bluetooth Low Energy technology, as well as, manage more efficiently resources are there in the health center.

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Section: Related Workmentioning

confidence: 99%

Prototype of Beacon-Mobile Application for Medical Appointment Scheduling Management

Zea

Solano

Rondon

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

View full text Add to dashboard Cite

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A Machine Learning Approach to Solve the Network Overload Problem Caused by IoT Devices Spatially Tracked Indoors

Carvalho

Sullivan

Almeida

et al. 2022

JSAN

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Currently, there are billions of connected devices, and the Internet of Things (IoT) has boosted these numbers. In the case of private networks, a few hundred devices connected can cause instability and even data loss in communication. In this article, we propose a machine learning-based modeling to solve network overload caused by continuous monitoring of the trajectories of several devices tracked indoors. The proposed modeling was evaluated with over a hundred thousand of coordinate locations of objects tracked in three synthetic environments and one real environment. It has been shown that it is possible to solve the network overload problem by increasing the latency in sending data and predicting intermediate coordinates of the trajectories on the server-side with ensemble models, such as Random Forest, and using Artificial Neural Networks without relevant data loss. It has also been shown that it is possible to predict at least thirty intermediate coordinates of the trajectories of objects tracked with R2 greater than 0.8.

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BLE Beacon Based Patient Tracking in Smart Care Facilities

Cited by 2 publications

References 5 publications

Prototype of Beacon-Mobile Application for Medical Appointment Scheduling Management

Prototype of Beacon-Mobile Application for Medical Appointment Scheduling Management

A Machine Learning Approach to Solve the Network Overload Problem Caused by IoT Devices Spatially Tracked Indoors

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