Movement primitives are usually assumed to extend over short temporal segments only, which begets the question of how these segments can be identified or defined. We developed  a motion segmentation algorithm based on Bayesian Binning. For this algorithm, we devised an observation model for each segment which encodes continuously varying functions. This new model predicts Gaussian-distributed observations with a mean that has a polynomial time dependence, and each segment has its own covariance matrix. The parameters of this observation model are learned with exact conjugate Gauss-Wishart posterior updates. We chose the polynomial time dependence, because in addition to the classic  power laws, certain human movements were shown to be well-captured by optimization models that maximize the smoothness of the trajectories. This can be mathematically expressed by the minimization of integrated jerk or by other time derivatives of position, e.g., acceleration, or snap, crackle etc. Such models imply that the trajectories will be well captured by polynomials of orders 3 (acceleration), 5 (jerk) and 7 (snap).