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While collecting raw sensor data has been made easier with advances in mobile sensing and computing, the process of data annotation remains a time-consuming and onerous process. This paper explores active learning as a way to minimize the labor-intensive task of labeling data. We train models with active learning in both offline and online settings with data from 4 publicly available activity recognition datasets and show that it performs comparably to or better than supervised methods while using around 10% of the training data. Moreover, we introduce a method based on conditional mutual information for determining when to stop the active learning process while maximizing recognition performance. 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