  We present a method to improve the accuracy of a foot-mounted,
zero-velocity-aided inertial navigation system (INS) by varying estimator
parameters based on a real-time classification of motion type. We train a
support vector machine (SVM) classifier using inertial data recorded by a
single foot-mounted sensor to differentiate between six motion types (walking,
jogging, running, sprinting, crouch-walking, and ladder-climbing) and report
mean test classification accuracy of over 90% on a dataset with five different
subjects. From these motion types, we select two of the most common (walking
and running), and describe a method to compute optimal zero-velocity detection
parameters tailored to both a specific user and motion type by maximizing the
detector F-score. By combining the motion classifier with a set of optimal
detection parameters, we show how we can reduce INS position error during mixed
walking and running motion. We evaluate our adaptive system on a total of 5.9
km of indoor pedestrian navigation performed by five different subjects moving
along a 130 m path with surveyed ground truth markers.
