Feature Subset Selection Utilizing BioMechanical Characteristics for Hand Gesture Recognition

Feature Subset Selection has become the focus of much research in areas of application for Multivariate Time Series (MTS). MTS data sets are common in many multimedia and medical applications such as gesture recognition, video sequence matching and EEG/ECG data analysis. MTS data sets are high dimensional as they consist of a series of observations of many variables at a time. The objective of feature subset selection is two-fold: providing a faster and more cost-effective process and a better understanding of the underlying process that generated the data. We propose a subset selection approach based on biomechanical characteristics, a simple yet effective technique for MTS. We apply our approach for recognizing ASL static signs using Neural Network and Multi-Layer Neural Network and show that we can maintain the same accuracy by selecting just 50% of the generated data.