Transformations of Gaussian Process priors for user matching

We describe the use of transformations of Gaussian Process (GP) priors to improve the context sensing capability of a system composed of a Kinect sensor and mobile inertial sensors. The Bayesian nonparametric model provides a principled mechanism for incorporating the low-sampling-rate position measurements and the high-sampling-rate derivatives in multi-rate sensor fusion which takes account of the uncertainty of each sensor type. The complementary properties of these sensors enable the GP model to calculate the likelihood of the observed Kinect skeletons and inertial data to identify individual users.