© 2015 Elsevier Inc. All rights reserved. Body sensor networks (BSNs) form an important class of devices where lightweight embedded processors and communication systems are tightly coupled with the human body. BSNs can provide researchers, care providers, and clinicians access to tremendously valuable information extracted from data that are collected in users' natural environments. With this information, one can monitor the progression of a disease, identify its early onset, or simply assess a user's wellness. One major obstacle to wide BSN deployment is maintaining and managing an enormous amount of sensing data. To address this issue, this chapter reviews existing mining techniques and describes a data-mining approach inspired by techniques in the areas of text and natural language processing. The approach is based on representing sensor readings with a sequence of characters called motion transcripts. Transcripts reduce complexity of the data significantly while maintaining morphological and structural properties of the physiological signals. To further take advantage of the signal's structure, the data-mining technique focuses on the characteristic transitions in the signals. These transitions are efficiently captured using the concept of n-grams. To facilitate a lightweight and fast mining approach, the overwhelmingly large number of n-grams is reduced via information gain (IG)-based feature selection. The effectiveness of the proposed approach is evaluated in terms of the speed of mining while maintaining an acceptable accuracy in terms of the F-score combining both precision and recall.