Closing The Gaps In Inertial Motion Tracking

Abstract

A rich body of work has focused on motion tracking techniques using inertial sensors, namely accelerometers, gyroscopes, and magnetometers. Applications of these techniques are in indoor localization, gesture recognition, inventory tracking, vehicular motion, and many others. This paper identifies room for improvement over today's motion tracking techniques. The core observation is that conventional systems have trusted gravity more than the magnetic North to infer the 3D orientation of the object. We find that the reverse is more effective, especially when the object is in continuous fast motion. We leverage this opportunity to design MUSE, a magnetometer-centric sensor fusion algorithm for orientation tracking. Moreover, when the object's motion is somewhat restricted (e.g., human-arm motion restricted by elbow and shoulder joints), we find new methods of sensor fusion to fully leverage the restrictions. Real experiments across a wide range of uncontrolled scenarios show consistent improvement in orientation and location accuracy, without requiring any training or machine learning. We believe this is an important progress in the otherwise mature field of IMU-based motion tracking.