A Deep Neural Network-Based Permanent Magnet Localization for Tongue Tracking

Permanent magnet localization (PML) is a nascent method of wireless motion tracking that can estimate the 5D state (3D position and 2D orientation) of a cylindrical magnet from its magnetic field. PML is well suited for applications where a wireless tracking is crucial, and in particular for tongue motion which opens up many new interesting applications. To allow its usage outside of a research lab, our tongue tracking system relies on the PML to avoid impeding tongue's natural motion due to its wireless tracking method and ensure safe use inside the mouth. Our tracking module is embedded in a headset to be portable and simple to use while being affordable by relying on the mass-produced components. The classical implementation of PML has many shortcomings that limit its practicality for realworld applications because it is computationally intensive due to its iterative algorithm, subject to local minimum convergence issues, and sensitive to its initial state and calibration parameters. Additionally, its physical model is an approximation that is only valid in limited tracking conditions. In this paper, we investigated the potential of deep learning to create a PML model for tongue tracking by training a feedforward neural network (3 layers, 100 neurons per layer) on a dataset composed of ~1.7 million states spanning a volume of 10 × 10 × 10 cm³. Our PML was validated on 337000 states in a 4 × 6 × 7 cm³ volume and tested on 100000 samples emulating a more natural tongue motion with curves and twists. A fully automated 5D positional stage was engineered by our team to collect these large datasets of the 5D magnet states. Our PML prediction, limited to the magnet's 3D positions in this paper, reaches positional errors of 1.4 mm (median) and 1.8 mm (Q3).