Tracking system for magnetic foreign bodies localization using a portable device
This paper presents a tracking system to find the position and yaw rotation of a handheld device based on the imaging of an adhesive marker attached to a person's body. Our goal is to provide accurate data that can be combined with data from a giant magnetoresistance sensor to be able to locate...
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2021-12-01
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917421002385 |
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doaj-55f580ecd2f04d72a9497c93ad8578382021-09-25T05:11:27ZengElsevierMeasurement: Sensors2665-91742021-12-0118100275Tracking system for magnetic foreign bodies localization using a portable deviceMarcos Rogozinski0Carlos Roberto Hall Barbosa1Elisabeth Costa Monteiro2Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, BrazilCorresponding author.; Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, BrazilPontifical Catholic University of Rio de Janeiro, Rio de Janeiro, BrazilThis paper presents a tracking system to find the position and yaw rotation of a handheld device based on the imaging of an adhesive marker attached to a person's body. Our goal is to provide accurate data that can be combined with data from a giant magnetoresistance sensor to be able to locate a foreign body inside a human body for surgical removal. We tested the system with a series of computer simulations and analyzed the results obtained. Our best setup provided a Root Mean Square Error of less than 1.6 mm for the x coordinate, less than 1 mm for the y coordinate, less than 1.2 cm for the z coordinate and less than 1.1° for the yaw rotation value in a test with wide variations of all values. The results obtained are promising and open the way for the combination of data with the portable magnetic sensor.http://www.sciencedirect.com/science/article/pii/S2665917421002385BiomagnetismCamera poseMagnetic foreign bodyGMRData fusionConvolutional neural networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Marcos Rogozinski Carlos Roberto Hall Barbosa Elisabeth Costa Monteiro |
spellingShingle |
Marcos Rogozinski Carlos Roberto Hall Barbosa Elisabeth Costa Monteiro Tracking system for magnetic foreign bodies localization using a portable device Measurement: Sensors Biomagnetism Camera pose Magnetic foreign body GMR Data fusion Convolutional neural networks |
author_facet |
Marcos Rogozinski Carlos Roberto Hall Barbosa Elisabeth Costa Monteiro |
author_sort |
Marcos Rogozinski |
title |
Tracking system for magnetic foreign bodies localization using a portable device |
title_short |
Tracking system for magnetic foreign bodies localization using a portable device |
title_full |
Tracking system for magnetic foreign bodies localization using a portable device |
title_fullStr |
Tracking system for magnetic foreign bodies localization using a portable device |
title_full_unstemmed |
Tracking system for magnetic foreign bodies localization using a portable device |
title_sort |
tracking system for magnetic foreign bodies localization using a portable device |
publisher |
Elsevier |
series |
Measurement: Sensors |
issn |
2665-9174 |
publishDate |
2021-12-01 |
description |
This paper presents a tracking system to find the position and yaw rotation of a handheld device based on the imaging of an adhesive marker attached to a person's body. Our goal is to provide accurate data that can be combined with data from a giant magnetoresistance sensor to be able to locate a foreign body inside a human body for surgical removal. We tested the system with a series of computer simulations and analyzed the results obtained. Our best setup provided a Root Mean Square Error of less than 1.6 mm for the x coordinate, less than 1 mm for the y coordinate, less than 1.2 cm for the z coordinate and less than 1.1° for the yaw rotation value in a test with wide variations of all values. The results obtained are promising and open the way for the combination of data with the portable magnetic sensor. |
topic |
Biomagnetism Camera pose Magnetic foreign body GMR Data fusion Convolutional neural networks |
url |
http://www.sciencedirect.com/science/article/pii/S2665917421002385 |
work_keys_str_mv |
AT marcosrogozinski trackingsystemformagneticforeignbodieslocalizationusingaportabledevice AT carlosrobertohallbarbosa trackingsystemformagneticforeignbodieslocalizationusingaportabledevice AT elisabethcostamonteiro trackingsystemformagneticforeignbodieslocalizationusingaportabledevice |
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1717368881595547648 |