Automatic Data Acquisition with Robots for Indoor Fingerprinting

Nastac, Dumitru Iulian; Lehan, Elena-Simona; Iftimie, Florentin Alexandru; Arsene, Octavian; Cramariuc, Bogdan

doi:10.1109/iccomm.2018.8484796

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…A robot developed by researchers in [7] is capable of generating a map using LiDAR sensors and simultaneous localization and mapping (SLAM) methods. The robot was equipped with an RGBD camera to improve the efficiency of the WiFi radio map, especially in featureless areas such as corridors.…”

Section: Introductionmentioning

confidence: 99%

Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation

et al. 2023

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Datasets used for artificial-neural-network and machine-learning applications play a vital role in the research and application of such techniques in solving real-life problems. The construction and availability of large datasets to be used in the off-line phase of ANN training is usually a crucial and time-consuming step towards system construction. In this work, a framework for autonomous construction of a diverse, extensive, and open dataset* with built-in redundancy is demonstrated. As part of the framework, a low-cost robot using off-the-shelf components is built that constructs the dataset autonomously. The robot includes a controller network with multiple WiFi-transceiver nodes for collecting received-signal-strength indicators (RSSIs) at various elevation points throughout the building. All nodes are configured with direct internet access to streamline the data collection towards an online database that is constructed as part of this framework. Preliminary validation and analysis of the dataset are discussed, and an exploration of the application domain of the dataset is carried out. Moreover, this paper investigates the effect of the height of the hand-held mobile WiFi antenna attached to the robot on the received power strength of the WiFi signal.

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Section: Introductionmentioning

confidence: 99%

Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation

et al. 2023

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A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems

Roy

Chowdhury

2021

J Intell Robot Syst

113

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Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

Lim

Yeo

Dahan

et al. 2021

J NeuroIntervent Surg

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BackgroundDelivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention.MethodsWe conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction.ResultsML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested.ConclusionsML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.

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Automatic Data Acquisition with Robots for Indoor Fingerprinting

Cited by 4 publications

References 19 publications

Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation

Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation

A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

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