A dynamic sliding window approach for activity recognition

Human activity recognition aims to infer the actions of one or more persons from a set of observations captured by sensors. Usually, this is performed by following a fixed length sliding window approach for the features extraction where two parameters have to be fixed: the size of the window and the shift. In this paper we propose a different approach using dynamic windows based on events. Our approach adjusts dynamically the window size and the shift at every step. Using our approach we have generated a model to compare both approaches. Experiments with public datasets show that our method, employing simpler models, is able to accurately recognize the activities, using fewer instances, and obtains better results than the approaches used by the datasets authors.