Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments

Bian, Sizhen; Hevesi, Péter; Christensen, Leif; Lukowicz, Paul

doi:10.3390/s21062218

Cited by 13 publications

(9 citation statements)

References 34 publications

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“…Oscillating magnetic field has been explored in a variety of HAR tasks, such as indoor location [125], finger tracking [126], human-computer-interaction [127], wearable social distance monitoring [128][129][130], etc. It could also be implemented for underwater positioning to enable the tracking or navigation of underwater-unmanned vehicles or divers [131].…”

Section: (B) Magnetic Fieldmentioning

confidence: 99%

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Bian

Liu

Zhou

et al. 2022

Sensors

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Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

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Section: (B) Magnetic Fieldmentioning

confidence: 99%

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Bian

Liu

Zhou

et al. 2022

Sensors

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“…Other studies are based on either computer simulations or downscaled laboratory experiments [ 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Bian et al [ 11 ] proposes an oscillating magnetic field-based indoor and underwater positioning system. The magnetic approach generates a bubble-formed magnetic field that is unaffected by environmental variation, unlike radio-wave-based positioning modalities.…”

Section: Introductionmentioning

confidence: 99%

In Situ Underwater Localization of Magnetic Sensors Using Natural Computing Algorithms

Alimi¹,

Fisher

Nahir³

2023

Sensors

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In the shallow water regime, several positioning methods for locating underwater magnetometers have been investigated. These studies are based on either computer simulations or downscaled laboratory experiments. The magnetic fields created at the sensors’ locations define an inverse problem in which the sensors’ precise coordinates are the unknown variables. This work addresses the issue through (1) a full-scale experimental setup that provides a thorough scientific perspective as well as real-world system validation and (2) a passive ferromagnetic source with (3) an unknown magnetic vector. The latter increases the numeric solution’s complexity. Eight magnetometers are arranged according to a 2.5 × 2.5 m grid. Six meters above, a ferromagnetic object moves according to a well-defined path and velocity. The magnetic field recorded by the network is then analyzed by two natural computing algorithms: the genetic algorithm (GA) and particle swarm optimizer (PSO). Single- and multi-objective versions are run and compared. All the methods performed very well and were able to determine the location of the sensors within a relative error of 1 to 3%. The absolute error lies between 20 and 35 cm for the close and far sensors, respectively. The multi-objective versions performed better.

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“…The hand tracking solution was proposed mainly considering two advantages of the modality. Firstly, low frequency oscillating magnetic field is a robust way for distance information abstracting since environmental variation could hardly deform the field [18]. Secondly, the proposed system is easy to deploy, and the cost is less than tens of dollars, enabling the magnetic field-based technique to be a generalized solution for 3D hand positioning Overall, we have the following two contributions: The general concept for using the oscillating magnetic field for 3D hand tracking, and the corresponding system optimized for reliable and accurate hand tracking, 2.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Field Based Hand Tracking

Bian¹,

Guo²,

Liu³

et al. 2022

Preprint

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Sensor-based 3D hand tracking is still challenging despite the massive exploration of different sensing modalities in the past decades. This work describes the design, implementation, and evaluation of a novel induced magnetic field-based 3D hand tracking system, aiming to address the shortcomings of existing approaches and supply an alternative solution. This system is composed of a set of transmitters for the magnetic field generation, a receiver for field strength sensing, and the Zigbee units for synchronization. In more detail, the transmitters generate the oscillating magnetic fields with a registered sequence, the receiver senses the strength of the induced magnetic field by a customized three axes coil, which is configured as the LC oscillator with the same oscillating frequency so that an induced current shows up when the receiver is located in the field of the generated magnetic field. Five scenarios are explored to evaluate the performance of the proposed system in hand tracking regarding the transmitters deployment: "in front of a whiteboard", "above a table", "in front of and in a shelf", "in front of the waist and chest", and "around the waist". The true-range multilateration method is used to calculate the coordinates of the hand in 3D space. Compared with the ground truth collected by a commercial ultrasound positioning system, the presented magnetic field-based system shows a robust accuracy of around ten centimeters with the transmitters deployed both off-body and on-body(in front of waist and chest), which indicates the feasibility of the proposed sensing modality in 3D hand tracking.

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Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments

Cited by 13 publications

References 34 publications

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

In Situ Underwater Localization of Magnetic Sensors Using Natural Computing Algorithms

Magnetic Field Based Hand Tracking

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