  A Bayesian zero-velocity detector for foot-mounted inertial navigation
systems is presented. The detector extends existing zero-velocity detectors
based on the likelihood-ratio test, and allows, possibly time-dependent, prior
information about the two hypotheses - the sensors being stationary or in
motion - to be incorporated into the test. It is also possible to incorporate
information about the cost of a missed detection or a false alarm.
Specifically, we consider an hypothesis prior based on the velocity estimates
provided by the navigation system and an exponential model for how the cost of
a missed detection increases with the time since the last zero-velocity update.
Thereby, we obtain a detection threshold that adapts to the motion
characteristics of the user. Thus, the proposed detection framework efficiently
solves one of the key challenges in current zero-velocity-aided inertial
navigation systems: the tuning of the zero-velocity detection threshold. A
performance evaluation on data with normal and fast gait demonstrates that the
proposed detection framework outperforms any detector that chooses two separate
fixed thresholds for the two gait speeds.
