Modeling of human motion through a discrete sequence of motion primitives, retaining elements of skillful or unique motion of an individual is addressed. Using wireless inertial motion sensors, a skeletal model of the fluid human gait was gathered. The posture of the human model is described by sets of Euler angles for each sample. An intrinsic classification algorithm known as minimum message length encoding (MML) is deployed to segment the stream of data and subsequently formulate certain Gaussian mixture models (GMM) that contain a plausible range of motion primitives. The removal of certain less seemingly important modes has been shown to significantly affect the fluidity of a gait cycle. The approach is described and the outcomes so far are provided.