Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Domingo, Jaime Duque; Gómez-García-Bermejo, Jaime; Zalama, Eduardo; Cerrada, Carlos; Valero, Enrique

doi:10.3390/s19245495

Cited by 6 publications

(4 citation statements)

References 43 publications

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“…Using only the information from the sensors, it is impossible to know which user performs the action. Therefore, in order to classify the activity and identify the position of each user inside the house, a number of Bluetooth beacons were installed in each room of the house [ 50 ]. These beacons measure the Bluetooth power emitted by a wearable device such as an activity wristband.…”

Section: The Sdhar-home Databasementioning

confidence: 99%

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Ramos

Domingo

Zalama

et al. 2022

Sensors

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Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user’s state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network.

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Section: The Sdhar-home Databasementioning

confidence: 99%

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Ramos

Domingo

Zalama

et al. 2022

Sensors

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“…It is possibly the most viable solution to provide fine-grained tracking capabilities to IPSs due to the high variability of the RSSI and other sensors used for positioning. In [ 27 ], the authors combine the coarse, fingerprint-based IPS with computer vision analysis from cameras in the scenario to provide a wide-area, fine-grained IPS. On the other hand, in [ 28 ], the authors use a mobile, camera-enabled device to provide a computer vision-based indoor navigation system to help blind and visually impaired people, an application that requires high accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Small World Graph Approach for an Efficient Indoor Positioning System

Lima

Guimarães²,

Santos

et al. 2021

Sensors

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The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this purpose, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample that needs to be classified must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large-scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, large-scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 98% when compared to the classic kNN and at least 80% when compared to tree-based approaches.

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“…The authors demonstrate how images can be leveraged for improving the distinction between lane lines at a short distance. In [26] authors propose the integration of CV with wireless sensors to perform a high accurate indoor localization, through the use of Wi-Fi signals emitted by the smartphones for fine users' positioning. An interesting perspective on the CV exploitation is also presented in [24], where the authors introduce the concepts of View to Communicate (V2C) and Communicate to View (C2V) paradigms.…”

Section: Introductionmentioning

confidence: 99%

BEST-RIM: A mmWave Beam Steering Approach Based on Computer Vision-Enhanced Reconfigurable Intelligent Metasurfaces

Loscrì

Rizza

Benslimane

et al. 2023

IEEE Trans. Veh. Technol.

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In this work, we consider Reconfigurable Intelligent Meta-surfaces (RIMs) to provide effective beam steering functionality, while enhancing the coverage of the fifth generation (5G) users in a urban city mobility context. In particular, we enable communications in the millimeter wave (mmWave) frequencies, that can occur simultaneously with other 5G communications. The main objective of this paper is to demonstrate the feasibility and advantages of an integrated Computed Vision (CV) approach for tuning the meta-atom states of a RIM structure working at mmWave frequency band, without estimating the interferes' radio channel. In particular, the CV system will feed a logic unit running a Machine Learning (ML) algorithm to compute in real-time the coding schemes, namely the sequence of binary states associated to each unit-cell of the RIM, in order to obtain the target reconfigurable radiation pattern. Specifically, a Genetic Algorithm (GA) is introduced to derive the most suitable radiation pattern for Beam Steering application, based on the input of a CV infrastructure. Results show the feasibility of such a kind of system, with an higher coverage achieved in dense scenarios, by improving the robustness against the potential blockages introduced by the mmWave technology. Moreover, we demonstrate the system is robust against the inaccuracy introduced by the CV and GA.

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Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Cited by 6 publications

References 43 publications

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

A Small World Graph Approach for an Efficient Indoor Positioning System

BEST-RIM: A mmWave Beam Steering Approach Based on Computer Vision-Enhanced Reconfigurable Intelligent Metasurfaces

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