  Untethered, inside-out tracking is considered a new goalpost for virtual
reality, which became attainable with advent of machine learning in SLAM. Yet
computer vision-based navigation is always at risk of a tracking failure due to
poor illumination or saliency of the environment. An extension for a navigation
system is proposed, which recognizes agents motion and stillness states with
87% accuracy from accelerometer data. 40% reduction in navigation drift is
demonstrated in a repeated tracking failure scenario on a challenging dataset.
