Skeleton Joints Moment (SJM): A Hand Gesture Dimensionality Reduction for Central Nervous System Interaction

Abstract

Recent breakthroughs with numerous visual experiences using mobile devices encourage the research of human-computer interaction (HCI) involving hand gesture recognition for Holograms, Virtual Reality, and Augmented Reality. The rise of these technologies allows educators in medical segments to apply new pedagogy by interacting with virtual content in a coherent learning environment. This paper proposed the Central Nervous System (CNS) interaction using the Skeleton Joints Moment (SJM) approach for dimension reduction with k Nearest Neighbour (k-NN) for hand gesture classification. Over the past few decades, researchers have proposed various techniques in dimension reduction. One of the methods is principal component analysis (PCA). Experimental results indicated that the SJM technique has similar accuracy to PCA, where both methods showed 96% of prediction using hand skeleton joints data. In addition, PCA has a higher uncertainty of mean error 0.75 compared to SJM at only 0.01. Furthermore, PCA has the worst complexity of $O(min(p^{3},n^{3}))$ where SJM $O(n/d)$ . Evaluation results using the T-Test showed a significant difference between SJM and PCA where $p < 0.05$ . Thus, there is evidence to reject the null hypothesis.

Keywords

• Hand gesture recognition
• dimensionality reduction
• machine learning
• hologram