Improving Reliability of Magnetic Localization Using Input Space Transformation

• Published in: IEEE sensors journal 2023-11, Vol.23 (22), p.28390-28398
• Main Authors: Yaldiz, Cem O., Sebkhi, Nordine, Bhavsar, Arpan, Wang, Jun, Inan, Omer T.
• Format: Article
• Language: English
• Subjects: Accuracy; Calibration; Distribution shift; End users; inertial measurement unit (IMU); Localization; Location awareness; machine learning; magnetic localization; Magnetic measurement; Magnetization; magnetometer; Magnetometers; Model testing; motion tracking; neural network; Reliability; Sensors; tongue tracking; Tracking; Training
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3320033

Body motion tracking for medical applications has the potential to improve quality of life for people with physical or speech motor disorders. Current solutions available in the market are either inaccurate, not affordable, or are impractical for a medical setting or at home. Magnetic localization can address these issues thanks to its high accuracy, simplicity of use, wearability, and use of inexpensive sensors such as magnetometers. However, sources of unreliability affect magnetometers to such an extent that the localization model trained in a controlled environment might exhibit poor tracking accuracy when deployed to end users. Traditional magnetic calibration methods, such as ellipsoid fit (EF), do not sufficiently attenuate the effect of these sources of unreliability to reach a positional accuracy that is both consistent and satisfactory for our target applications. To improve reliability, we developed a calibration method called post-deployment input space transformation (PDIST) that reduces the distribution shift in the magnetic measurements between model training and deployment. In this article, we focused on change in magnetization or magnetometer as sources of unreliability. Our results show that PDIST performs better than EF in decreasing positional errors by a factor of \sim 3 when magnetization is distorted, and up to \sim 7 when our localization model is tested on a different magnetometer than the one it was trained with. Furthermore, PDIST is shown to perform reliably by providing consistent results across all our data collection that tested various combinations of the sources of unreliability.