Evolution of Indoor Positioning Technologies: A Survey

Brena, Ramón F.; García-Vázquez, Juan Pablo; Galván-Tejada, Carlos E.; Muñoz-Rodríguez, D.; Vargas-Rosales, César; Fangmeyer, James

doi:10.1155/2017/2630413

Cited by 361 publications

(290 citation statements)

References 77 publications

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“…As stated from literature evidences, the user indoor location could be estimated by means of different technologies: cameras, infrared, ultrasound, ZigBee, Radio-Frequency Identification, Bluetooth [4,26]. For the best or our knowledge, few works have fused the information on indoor user location with wearable systems to increase the performance of the recognition tasks.…”

Section: Vital Signsmentioning

confidence: 99%

Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

Fiorini

Bonaccorsi

Betti

et al. 2018

AIS

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Abstract. Thanks to the pervasiveness of smart technologies, researchers could aggregate data and investigate user's activities thus to deliver personalized home-care services. Activity recognition system have been widely developed, however some challenges still need to be addressed. This paper presents a system where information on body movement, vital signs and user indoor location are aggregated to improve the activity recognition. The system was tested in a realistic environment with a total of 3279 instances acquired from ten healthy users. These results encouraged the use of this approach in activity recognition applications, indeed, the overall accuracy values are satisfactory increased.

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Section: Vital Signsmentioning

confidence: 99%

Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

Fiorini

Bonaccorsi

Betti

et al. 2018

AIS

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“…However, industrial environments usually hinder the use of traditional radio‐frequency–based (RF‐based) localization methods, as these environments are mostly full of obstacles of different materials, shapes, and sizes that may obstruct signals between emitters and receivers. Many RF‐based localization methods require a nonobstructed path between a transmitter and a receiver; and the signal strength, signal delay, and angle of arrival of electromagnetic wave propagation will be believably handicapped by severe levels of impairments and interferences in industrial indoor facilities …”

Section: Introductionmentioning

confidence: 99%

“…A promising approach to obtain localization precision around few centimeters or less is to use computer vision methods in industrial environments . Commonly, these environments present many cameras, which are used for monitoring and surveillance purposes.…”

Section: Introductionmentioning

confidence: 99%

Programmable intelligent spaces for Industry 4.0: Indoor visual localization driving attocell networks

Carmo

Vassallo

Queiroz

et al. 2019

Trans Emerging Tel Tech

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Real‐time and mission‐critical applications for Industry 4.0 demand fast and reliable communication. Therefore, knowing devices' location is essential, but GPS is of little use indoors, whereas electromagnetic impairments and interferences demand new approaches to ensure reliability. The challenges include real‐time feedback with end‐to‐end (E2E) low latency; high data density due to large number of IoT devices per area; and smaller communication cells, which increases the handover frequency and complexity. To tackle these issues, we introduce a programmable intelligent space (PIS) to deploy attocells, enable E2E programmability, and provide a precise computer vision localization system and networking programmability based on software‐defined networking. To validate our approach, experiments were conducted, controlling a mobile robot through a trajectory. We demonstrate the need for higher camera frame rate to achieve tighter precision, evaluating different trade‐offs on localization, bandwidth, and latency. Results have shown that PIS wireless attocell handover achieves seamlessly mobile communication, delivering packets within the deadline window, with similar performance to a no handover baseline.

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“…Although indoor positioning is difficult to conduct using the global position service (GPS) because GPS signals cannot be received, technical studies are now being conducted to provide various indoor location-based services [1]. In particular, smartphones with various sensors such as accelerometers, gyroscopes, magnetometers, and barometers have become popular, and network infrastructures (e.g., Wi-Fi) have expanded to a point at which location-based services are achievable for indoor spaces [2].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Kang

Lee

Eom

2018

Sensors

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We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments.

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Evolution of Indoor Positioning Technologies: A Survey

Cited by 361 publications

References 77 publications

Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

Programmable intelligent spaces for Industry 4.0: Indoor visual localization driving attocell networks

Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

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