A Survey of 3D Indoor Localization Systems and Technologies

Sesyuk, Andrey; Ioannou, Stelios; Raspopoulos, Marios

doi:10.3390/s22239380

Cited by 38 publications

(21 citation statements)

References 116 publications

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“…The smart bracelet, in combination with cloud technology, in the actual application, can select different monitoring parameters according to requirements, formulate appropriate algorithms, and develop the threshold value of early warning to realize auxiliary diagnostic decisions. At the same time, in terms of indoor positioning technology, it also adopts Bluetooth AOA technology, which has higher precision and three-dimensional positioning technology compared with ZigBee wireless network transmission protocol [6,10]. BAN can comply with the needs of intelligent management and monitoring of medical health information and public health security, realize the interaction between individuals and medical personnel, medical institutions, medical equipment, successful disease prevention, and personal health management, and promote the informatization and intelligence of medical services [11,12].…”

Section: Discussionmentioning

confidence: 99%

Application of smart bracelet based on 5G and body area network technology in monitoring key populations in the ward

Wang

Cai

et al. 2023

Preprint

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Background: Using 5th Generation Mobile Communication (5G) and cloud Technology to design a smart bracelet for key populations during the epidemic, improve the efficiency of epidemic prevention and control, and limit epidemic transmission. Methods: A thermosensitive metal temperature sensor and positioning chip were used to develop a smart bracelet. The smart bracelet monitored the temperature changes and real-time positioning of 40 patients at different time points. To validate the efficacy of smart bracelets in monitoring key populations, a comprehensive assessment of their consistency and accuracy was performed using fixed-point data collected by high-precision mercury thermometers and cameras. Results: The body area network (BAN)-based smart bracelet's temperature monitoring during the COVID-19 epidemic was compared to that of a mercury thermometer at different time points of the key population, with ICC=0.883, showing a high consistency and accuracy. Analysis of the real-time positioning data obtained from smart bracelets and camera monitoring, yielded highly accurate outcomes, with a Kappa value of 0.494, a sensitivity of 46.4%, a specificity of 98.7%, and an accuracy of 0.968. Conclusion: 5G、BAN and other technology-based smart bracelets are feasible for temperature and displacement monitoring of key populations, can replace the duties of medical personnel in certain wards, reduce contact, and have the role of reducing hospital infection.

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Section: Discussionmentioning

confidence: 99%

Application of smart bracelet based on 5G and body area network technology in monitoring key populations in the ward

Wang

Cai

et al. 2023

Preprint

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“…Trilateration is a method used to establish the position of a target point by taking distance or angle measurements from at least three known points. When determining indoor positioning in a 3D environment, 5 it is necessary to consider three known points,

A\left({x}_1,{y}_1,{z}_1\right)

B\left({x}_2,{y}_2,{z}_2\right)

, and

C\left({x}_3,{y}_3,{z}_3\right)

, as illustrated in Figure 2.…”

Section: Related Workmentioning

confidence: 99%

Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement

Kim,

You

et al. 2024

Computational Intelligence

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Indoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix‐based RSSI indoor positioning (M‐RIP) for initial location estimation and feature‐based image indoor positioning (F‐IIP) for position determination via image feature matching. Furthermore, extended area‐based post‐processing (EA‐PP) is employed to augment M‐RIP's precision and minimize image matching computation in F‐IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.

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“…Indoor positioning technology has developed to support a variety of services making use of the sensors and protocols embedded in mobile devices. After the emergence of iBeacon [ 1 ] and Eddystone [ 2 ], attempts to detect position in indoor environments using Bluetooth have increased compared to WiFi-based techniques, mainly because Bluetooth is designed to operate with low power consumption [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A TinyML Deep Learning Approach for Indoor Tracking of Assets

Avellaneda

Méndez

Fortino

2023

Sensors

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Positioning systems have gained paramount importance for many different productive sector; however, traditional systems such as Global Positioning System (GPS) have failed to offer accurate and scalable solutions for indoor positioning requirements. Nowadays, alternative solutions such as fingerprinting allow the recognition of the characteristic signature of a location based on RF signal acquisition. In this work, a machine learning (ML) approach has been considered in order to classify the RSSI information acquired by multiple scanning stations from TAG broadcasting messages. TinyML has been considered for this project, as it is a rapidly growing technological paradigm that aims to assist the design and implementation of ML mechanisms in resource-constrained embedded devices. Hence, this paper presents the design, implementation, and deployment of embedded devices capable of communicating and sending information to a central system that determines the location of objects in a defined environment. A neural network (deep learning) is trained and deployed on the edge, allowing the multiple external error factors that affect the accuracy of traditional position estimation algorithms to be considered. Edge Impulse is selected as the main platform for data standardization, pre-processing, model training, evaluation, and deployment. The final deployed system is capable of classifying real data from the installed TAGs, achieving a classification accuracy of 88%, which can be increased to 94% when a post-processing stage is implemented.

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A Survey of 3D Indoor Localization Systems and Technologies

Cited by 38 publications

References 116 publications

Application of smart bracelet based on 5G and body area network technology in monitoring key populations in the ward

Application of smart bracelet based on 5G and body area network technology in monitoring key populations in the ward

Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement

A TinyML Deep Learning Approach for Indoor Tracking of Assets

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