In this research we developed a wearable, augmented motion feedback system for ubiquitous training and motion assessment in mid-level ski jumping. Ski jump motion data captured with a set of inertial sensors were first transformed into meaningful kinematic motion information using an extensive processing system. Next, derived segment orientations, joint positions and joint angles were used to build and train motion knowledge on the base of the sport's common style and judging criteria. This intelligent machine knowledge was then applied to identify specific style information within incoming motion data that could be provided to the athlete as augmented motion feedback via a mobile training application. System validations on a set of test jumping data showed that style errors could be recognized and displayed well by the implemented system. We therefore believe the system to be suitable for the provision of kinematic motion feedback that could not be obtained without an extensive training support environment otherwise. Adding a real-time environment for athletesystem communication, this could lead to the creation of an ubiquitous training support application in future.