Using data dimensionality reduction for recognition of incomplete dynamic gestures

Continuous gesture spotting is a major topic in human-robot interaction (HRI) research. Human gestures are captured by sensors that provide large amounts of data that can be redundant or incomplete, correlated or uncorrelated. Data dimensionality reduction (DDR) techniques allow to represent such data in a low-dimensional space, making the classification process more efficient. This study demonstrates that DDR can improve the classification accuracy and allows the classification of gesture patterns with incomplete data, i.e., with the initial 25%, 50% or 75% of data representing a given dynamic gesture (DG) time series of positional and hand shape data. Re-sampling raw data with bicubic interpolation and principal component analysis (PCA) were used as DDR methods. The performance of different classifiers is compared in the classification 95 different signs of the UCI Australian Sign Language (High Quality) Dataset. Experimental tests indicate that the use of PCA-based features result in a classification accuracy that is higher with 25% of gesture data (93% accuracy) than with 100% of gesture data (82% accuracy). These results were obtained from a non-trained data set and the recognized gestures are used to control a robot in an collaborative process. © 2017 Elsevier B.V. All rights reserved.