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Model-based activity recognition has been recently proposed as an alternative to signal-oriented recognition. Such model-based approaches seem attractive due to their ability to enable user-independent activity recognition and due to their improved robustness to signal-variation. The first goal of this paper is therefore to systematically analyze the benefit of body-model derived primitives in different sensor settings for multi activity recognition. Furthermore we propose a new body-model based approach using accelerometer sensors only thereby reducing the sensor requirements significantly. Results on a 20 activity dataset indicate that body-model based approaches consistently improve results over signal-oriented approaches.