Preliminary Assessment of Accurate Motion Detection via Magnetic Tracking Toward Wearable Technologies

Tracking permanent magnets represents a low-footprint and passive approach to monitoring objects or human motion by attaching or embedding magnets therein. Recent tracking techniques achieved high-bandwidth detection considering a simplified model for the magnetic sources, i.e., the dipole model. Nonetheless, such a model can lead to inaccurate results any time a non-spherical magnet approaches the sensor array. Here, we present a novel tracking algorithm based on an analytical model for permanent magnet cylinders with uniform arbitrary magnetization. By means of a physical system mounting 20 magnetometers, we compared the tracking accuracy obtained with our algorithm vs. results obtained by using the dipole model and with respect to a ground-truth reference. With a single magnetic target, our algorithm can significantly lower position (up to 0.68 mm) and orientation errors (up to 2.5°) while enabling online tracking (computation time below 19 ms). We also accurately tracked two magnets, by obtaining a reduction in position error (up to 0.92 mm) vs. the dipole-based algorithm. These findings broaden the applicability of accurate magnetic tracking to real-time applications, facilitating the tracking of multiple magnetic targets in proximity of the magnetic sensors. This advancement opens avenues for applications in wearable devices, advancing the field of motion detection beyond traditional inertial measurement units.

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Publication Details

Journal
IEEE Transactions on Medical Robotics and Bionics
Published
2024-11-21
DOI
https://doi.org/10.1109/tmrb.2024.3504003
Citations
5
Primary Topic
Augmented Reality Applications
Type
article
Field-Weighted Citation Impact
1.25

Funders

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article

Preliminary Assessment of Accurate Motion Detection via Magnetic Tracking Toward Wearable Technologies

Lorenzo Masia, Federico Masiero, Valerio Ianniciello, Edoardo Sinibaldi et al.
5 citations
IEEE Transactions on Medical Robotics and Bionics
Augmented Reality Applications
1.25
article

Preliminary Assessment of Accurate Motion Detection via Magnetic Tracking Toward Wearable Technologies

Lorenzo Masia, Federico Masiero, Valerio Ianniciello, Edoardo Sinibaldi, Christian Cipriani, Roberto Raeli
article en
5 citations

Abstract

Tracking permanent magnets represents a low-footprint and passive approach to monitoring objects or human motion by attaching or embedding magnets therein. Recent tracking techniques achieved high-bandwidth detection considering a simplified model for the magnetic sources, i.e., the dipole model. Nonetheless, such a model can lead to inaccurate results any time a non-spherical magnet approaches the sensor array. Here, we present a novel tracking algorithm based on an analytical model for permanent magnet cylinders with uniform arbitrary magnetization. By means of a physical system mounting 20 magnetometers, we compared the tracking accuracy obtained with our algorithm vs. results obtained by using the dipole model and with respect to a ground-truth reference. With a single magnetic target, our algorithm can significantly lower position (up to 0.68 mm) and orientation errors (up to 2.5°) while enabling online tracking (computation time below 19 ms). We also accurately tracked two magnets, by obtaining a reduction in position error (up to 0.92 mm) vs. the dipole-based algorithm. These findings broaden the applicability of accurate magnetic tracking to real-time applications, facilitating the tracking of multiple magnetic targets in proximity of the magnetic sensors. This advancement opens avenues for applications in wearable devices, advancing the field of motion detection beyond traditional inertial measurement units.

IEEE Transactions on Medical Robotics and BionicsVol. 7(1)
Scuola Superiore Sant'Anna (IT), Heidelberg University (DE), Italian Institute of Technology (IT), Technical University of Munich (DE)
European Commission, H2020 Marie Skłodowska-Curie Actions
Openalex Percentile: Top 18%
Augmented Reality Applications
1.25
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