Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition
This work addresses the need for efficient and accurate activity classification for applications like health monitoring, but it is incremental as it builds on existing template-based methods.
The paper tackled human activity recognition by proposing a Dynamic Time Warping template selection method to avoid complex feature extraction, achieving competitive classification accuracy on simulated and real smartphone data.
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.