Robust tracking of periodic motion in the plane using inertial sensor data

We propose a robust method for estimating the orientation and displacement of an inertial measurement unit undergoing planar periodic motion. Such movements is a common approximation to human gait and running. We formulate the problem introducing a sparse vector of outlier errors and l1-regularization. The problem thus becomes robust to outliers in the data. The problem can be rewritten as a quadratic programming problem which can be solved efficiently using existing software for convex optimization.