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Accurate and computationally efficient means for classifying human activitieshave been the subject of extensive research efforts. Most current researchfocuses on extracting complex features to achieve high classification accuracy.We propose a template selection approach based on Dynamic Time Warping, suchthat complex feature extraction and domain knowledge is avoided. We demonstratethe predictive capability of the algorithm on both simulated and realsmartphone data.

Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition