Proceedings on Automation in Medical Engineering
Vol. 2 No. 1 (2023): Proc AUTOMED

Measurement technology and diagnostics, ID 745

RNN-based State and Parameter Estimation for Sparse Magnetometer-free Inertial Motion Tracking

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Simon Bachhuber (FAU Erlangen-Nürnberg), Ive Weygers (FAU Erlangen-Nürnberg), Thomas Seel (FAU Erlangen-Nürnberg)

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Inertial measurement units are widely used for motion tracking in numerous applications. Combining magnetometer- free sensor fusion with sparse sensor setups yields advantages in cost, effort, and usability but makes observer design a challenging task. Recently, a data-driven solution that utilizes recurrent neural networks as observers (RNNOs) has been developed. There, it is assumed that the geometry of the kinematic chain (lengths of segments, sensor-to-segment positions) is known. However, in practice the geometry is often unknown. Here, we show that RNNO can estimate the relative pose of a three segment kinematic chain with double hinge joints while (implicitly) identifying its geometry.

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