Comparative performance exploration of different machine learning and deep learning algorithms for classification of hand wrist gestures

Abstract

In the current scenario, reliable recognition and classification of hand wrist gestures are gaining high demand for numerous applications including health care application, for Sign language recognition, and in robotics for managing mobile robots etc. This paper highlights the comparative evaluation of various machine learning and deep learning algorithms for classification of hand wrist gestures in terms of classification accuracies, confusion matrix, recall, and precision. This research presents the experiment conducted on ten different subjects demonstrates six different hand wrist gestures for creation of dataset based on variation in scattering parameters of designed wearable Antennas.