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In this paper, the authors present a framework that allows for the automatic identification of sporting activities using commonly available Smartphone's. They extract discriminative informational features from Smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, Smartphone were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus they examine classifiers from each of the most widely used classifier families.
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