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The use of wearable sensors for human activity monitoring and recognition is becoming an important technology due to its potential benefits to our daily lives. In this paper, we present a sparse representation-based human activity modeling and recognition approach using wearable motion sensors. Our approach first learns an overcomplete dictionary to find the motion primitives shared by all activity classes. Activity models are then built on top of these motion primitives by solving a sparse optimization problem. Experiments on a dataset including nine activities and fourteen subjects show the advantages of using sparse representation for activity modeling and demonstrate that our approach achieves a better recognition performance compared to the conventional motion primitive-based approach.