Controller position tracking using inertial measurement units and machine learning

Abstract

A system having at least a first component and a second component positioned at different locations on a user's body (e.g., on the user's head and held on the user's hand). Each component includes at least one inertial measurement unit (IMU) configured generate measurements indicating acceleration and angular rate data. The generated measurements of the IMUs are used with ground truth information indicating the positions of the first and second component to generate a set of training data to train a neural network configured to predict a relative position between the first and second components based on IMU measurements received over a predetermined time period. Because the neural network is trained based upon movements of a human user, the neural network model takes into account physiological constraints of the user in determining how the set of potential positions of the different components may change over time, reducing potential error.