Multibody motion tracking using machine learning-informed physics models and inertial measurement units
AssigneeWilliam Marsh Rice University
InventorBenjamin J. Fregly
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Abstract
A computer-implemented method for calculating multibody motion data includes obtaining measured IMU data time histories with a plurality of inertial measurement units and using computer-based algorithm incorporating a machine-learning informed kinetic model to generate the multibody motion data. A system for generating motion data from a multibody system includes include a plurality of inertial measurement units, each configured to be placeable on a corresponding segment of the multibody system and a computer system comprising a processor configured to implement the computer-based algorithm.
Claims16
A computer-implemented method for calculating multibody motion data, comprising:
- obtaining measured IMU data time histories with a plurality of inertial measurement units;
- inputting the measured IMU data time histories and a guess for a plurality of joint position design variables into an analytical function to calculate joint position, velocity, and acceleration time histories;
- inputting the calculated joint position, velocity, and acceleration time histories into a kinematic model to calculate kinematic model predictions of IMU angular velocities and gravity-corrupted linear accelerations;
- inputting the measured IMU data time histories into a machine learning model to calculate machine learning model predictions of joint positions, velocities, and accelerations;
- determining optimal values for the plurality of joint position design variables by optimizing the guess for the plurality of joint position design variables using a cost function, wherein the cost function comprises a first type of error associated with matching IMU angular velocities and gravity-corrupted linear accelerations and a second type of error associated with matching joint positions, velocities, and accelerations;
- and
- generating the multibody motion data using the optimal values for the plurality of joint position design variables.
A system for generating motion data from a multibody system, comprising:
- a plurality of inertial measurement units, each configured to be placed on a corresponding segment of the multibody system;
- and
- a computer system comprising a processor configured to:
- input measured IMU data time histories and a guess for a plurality of joint position design variables into an analytical function to calculate joint position, velocity, and acceleration time histories;
- input the calculated joint position, velocity, and acceleration time histories into a kinematic model to calculate kinematic model predictions of IMU angular velocities and gravity-corrupted linear accelerations;
- input measured IMU data time histories into a machine learning model to calculate machine learning model predictions of joint positions, velocities, and accelerations;
- determine optimal values for the plurality of joint position design variables by optimizing the guess for the plurality of joint position design variables using a cost function, wherein the cost function comprises a first type of error associated with matching IMU angular velocities and gravity-corrupted linear accelerations and a second type of error associated with matching joint positions, velocities, and accelerations;
- and
- generate the motion data from a multibody system using the optimal values for the plurality of joint position design variables.
Dependencies are inferred from the claim text.