Orientation estimation for instrumented helmet using neural networks

Abstract

This work presents an integrated solution for head orientation estimation, which is a critical component for applications of virtual and augmented reality systems. The proposed solution builds upon the measurements from the inertial sensors and magnetometer added to an instrumented helmet, and an orientation estimation algorithm is developed to mitigate the effect of bias introduced by noise in the gyroscope signal. Convolutional Neural Network (CNN) techniques are introduced to develop a dynamic orientation estimation algorithm with a structure motivated by complementary filters and trained on data collected to represent a wide range of head motion profiles. The proposed orientation estimation method is evaluated experimentally and compared to both learning and non-learning-based orientation estimation algorithms found in the literature for comparable applications. Test results support the advantage of the proposed CNN-based solution, particularly for motion profiles with high acceleration disturbance that are characteristic of head motion.