GPS and IMU Fusion for Human Gait Estimation

Abstract

This paper proposes a framework for fusing information coming from an independent inertial measurement unit (IMU) and global positioning system (GPS) to deliver robust estimation of human gait. Because these two sensors provide very different kinds of data at different scales and frequencies, a novel approach which fuses global trajectory estimates and back-propagates this information to correct step vectors is put forth here. In several high-fidelity simulations, the proposed technique is shown to improve step estimation error up to 40% in comparison with an IMU-only approach. This work has implications for not only in-the-field biomechanics research, but also cooperative field robotic systems where it may be critical to accurately monitor a person’s position and state in real-time.