Enabling artificial systems to recognize human actions is a requisite to develop intelligent assistance systems that are able to instruct and supervise users in accomplishing tasks. In order to enable an assistance system to be wearable, head-mounted cameras allow to perceive a scene visually from a user's perspective. But realizing action recognition without any static sensors causes special challenges. The movement of the camera is directly related to the user's head motion and not controlled

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... by the system. In this paper we present how a trajectory-based action recognition can be combined with object recognition, visual tracking, and a background motion compensation to be applicable in such a wearable assistance system. The suitability of our approach is proved by user studies in an object manipulation scenario.