Multivariate Time Series Classification Using Dynamic Time Warping
  Template Selection for Human Activity Recognition
  Accurate and computationally efficient means for classifying human activities
have been the subject of extensive research efforts. Most current research
focuses on extracting complex features to achieve high classification accuracy.
We propose a template selection approach based on Dynamic Time Warping, such
that complex feature extraction and domain knowledge is avoided. We demonstrate
the predictive capability of the algorithm on both simulated and real
smartphone data.
