A Comparison of Computational Intelligence Techniques for Real-Time Discrete Multivariate Time Series Classification of Conducting Gestures

Abstract

Gesture classification is a computational process that can identify and classify human gestures. More specifically, gesture classification is often a discrete multivariate time series classification problem and various computational intelligence solutions have been developed for these problems. It is difficult to determine which existing techniques and approaches to algorithms will produce the most effective solutions for discrete multivariate time series classification problems. In this study, we compare twelve different classification algorithms to report which techniques and approaches are most effective for recognizing conducting beat pattern gestures. After performing 10-fold cross-validation tests on twelve commonly used algorithms, the results show that of the algorithms tested, the most accurate were RNN, LSTM, and DTW; all of which had an accuracy of 100%. We found that in general, algorithms which can take in a dynamic sequence input and classification algorithms that are discriminative performed consistently well, while their counterparts varied in performance. From these results we determine that when selecting a computational intelligence technique to solve these classification problems, it would be advantageous to consider the top performing algorithms along with furthering research into new dynamic input and discriminative algorithms.