Joint Magnetic Calibration and Localization Based on Expectation Maximization for Tongue Tracking.
Joint Magnetic Calibration and Localization Based on Expectation Maximization for Tongue Tracking.
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DOI：
10.1109/tbme.2017.2688919
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发表时间：
2018-01
期刊：
影响因子：
--
通讯作者：
Ghovanloo M
中科院分区：
文献类型：
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作者：
Lu J;Yang Z;Okkelberg KZ;Ghovanloo M
舌迹追踪在语言治疗和语言学习中有着广泛的应用，有助于研究人员对语言机制有价值的了解。无线定位技术，包括在三维口腔空间内跟踪一个小的磁性示踪剂，提供了一种低成本和方便的方法来捕捉舌头的运动学。在实际应用中，该技术需要对跟踪系统中使用的三轴磁传感器进行精确校准。数据驱动的校准依赖于磁示踪剂的轨迹和环境噪声，这些轨迹随时间和空间的变化而变化。在贝叶斯框架下，建立了示踪运动和噪声磁测量的运动学模型，提出了一种基于期望最大化（EM）的联合标定与定位（JCL）算法，该算法采用unscented Rauch-Tung-Striebel平滑（URTSS）进行示踪定位，采用曲线搜索算法进行传感器标定。通过对小型磁性示踪剂（直径6.05 mm，厚度1.25 mm，残余感应量14800 G）舌形跟踪系统的测量，JCL算法对示踪剂位置估计的平均均方根误差为0.45 mm，对示踪剂方向估计的平均均方根误差为2.33°，显著低于单独标定和定位算法。结果表明，JCL有助于提高系统的定位精度。演示了一种潜在的高精度舌头跟踪方法。
Tongue 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 3-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. In 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 (URTSS) is employed for tracer localization and the curvilinear search algorithm is applied for sensor calibration. Based on measurements conducted on our tongue tracking system with a small magnetic tracer (diameter: 6.05 mm, thickness: 1.25 mm, residual induction: 14800 G), the JCL algorithm achieves averaged root mean square error of 0.45 mm for tracer position estimation and 2.33° for tracer orientation estimation, which are significantly lower than those of the separate calibration and localization algorithms. These results show JCL can help improve the localization accuracy of this system. A potentially high precision tongue tracking method is demonstrated.