Magnetic interference in the motion capture environment is caused primarily by ferromagnetic objects and current-carrying devices disturbing the ambient, geomagnetic field. Inertial sensors gather magnetic data to determine and stabilize their global heading estimates, and such magnetic field disturbances alter heading estimates. This decreases orientation accuracy and therefore decreases motion capture accuracy. The often used Kalman Filter approach deals with magnetic interference by ignoring the magnetic data during periods interference is encountered, but this method is only effective when the disturbances are ephemeral, and cannot not retroactively repair data from disturbed time periods.
The objective of this research is to develop a method of magnetic interference mitigation for environments where magnetic interference is the norm rather than the exception. To the knowledge of this author, the ability to use inertial and magnetic sensors to capture accurate, global, and drift-free orientation data in magnetically disturbed areas has yet to be developed. Furthermore there are no methods known to this author that are able to use data from undisturbed time periods to retroactively repair data from disturbed time periods. The investigation begins by exploring the use of magnetic shielding, with the reasoning that application of shielding so as to impede disturbed fields from affecting the inertial sensors would increase orientation accuracy. It was concluded that while shielding can mitigate the effect of magnetic interference, its application requires a tedious trial and error testing that was not guaranteed to improve results. Furthermore, shielding works by redirecting magnetic field lines, increasing field complexity, and thus has a high potential to exacerbate magnetic interference.
Shielding was determined to be an impractical approach, and development of a magnetic inference mitigation algorithm began. The algorithm was constructed such that magnetic data would be filtered before inclusion in the orientation estimate, with the result that exposure in an undisturbed environment would improve estimation, but exposure to a disturbed environment would have no effect. The algorithm was designed for post-processing, rather than real-time use as Kalman Filters are, which enabled magnetic data gathered before and after a time point could affect estimation.
The algorithm was evaluated by comparing it with the Kalman Filter approach of the company XSENS, using the gold standard of optical motion capture as the reference point. Under the tested conditions of stationary periods and smooth planar motion, the developed algorithm was resistant to magnetic interference for the duration of testing, while the Kalman Filter began to degrade after approximately 15 seconds. In a 190 second test, of which 180 were spent in a disturbed environment, the developed algorithm resulted in 0.4 degrees of absolute error, compared to the of the Kalman Filter’s 78.8 degrees.
The developed algorithm shows the potential for inertial systems to be used effectively in situations of consistent magnetic interference. As the benefits of inertial motion capture make it a more attractive option than optical motion capture, immunity to magnetic interference significantly expands the usable range of motion capture environments. Such expansion would be beneficial for motion capture studies as a whole, allowing for the cheaper, more practical inertial approach to motion capture to supplant the more expensive and time consuming optimal option.