Evaluation of techniques for signature classification from accelerometer and gyroscope data

In this paper, we present an exhaustive comparison of techniques for classification of signature data extracted from gyroscope and accelerometer devices. Since there exists large pool of classifiers and features for this kind of data, in order to provide a guide in choosing a particular setup, we decided to explore performance of these methods in a comparative study, which is a missing factor of current works on the topic. Also, we propose a framework for the combination of evaluated techniques in order to achieve a higher precision of the final classifier. The evaluated factors are: transformation of the time-series data into a fixed-size vector, classification methods and the performance of generative techniques without fixed-size input.