ArticleIEEE transactions on bio-medical engineering2018
Joint Magnetic Calibration and Localization Based on Expectation Maximization for Tongue Tracking.
Article in IEEE transactions on bio-medical engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Two-stage cylindrical magnetic localization with neural network calibration for wearable capsule endoscopy.Scientific reports · 2026Article
- Cross-modal attention model integrating tongue images and descriptions: a novel intelligent TCM approach for pathological organ diagnosis.Frontiers in physiology · 2025Article
- Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer.Sensors (Basel, Switzerland) · 2021Article
- Advances in automated tongue diagnosis techniques.Integrative medicine research · 2019Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundTongue tracking, which helps researchers gain valuable insights into speech mechanism, has many applications in speech therapy and language learning. The wireless localization technique, which involves tracking a small magnetic tracer within the 3-D oral space, provides a low cost and convenient approach to capture tongue kinematics. In practice, this technique requires accurate calibration of three-axial magnetic sensors used in the tracking system. The data-driven calibration depends on the trajectories of magnetic tracer and the ambient noise, which may change across time and space.
methodsIn this paper, we model the kinematics of tracer movement and the noisy magnetic measurements in a Bayesian framework, then present a joint calibration and localization (JCL) algorithm based on expectation maximization (EM), where the unscented Rauch-Tung-Striebel smoother is employed for tracer localization and the curvilinear search algorithm is applied for sensor calibration.
resultsBased on measurements conducted on our tongue tracking system with a small magnetic tracer (diameter: 6.05 mm, thickness: 1.25 mm, residual induction: 14 800 G), the JCL algorithm achieves averaged root mean square error of 0.45 mm for tracer position estimation and for tracer orientation estimation, which are significantly lower than those of the separate calibration and localization algorithms.
conclusionThese results show that JCL can help improve the localization accuracy of this system. SIGNIFICANCE: A potentially high precision tongue tracking method is demonstrated.
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