Haptic feature selection using Statistical Analysis for Haptic Object Reconstruction

Abstract

Object reconstruction in the haptic dimension is crucial in achieving realization in the virtual world. Prior studies have employed the conventional spring damper model to interpret object behavior, with motion parameters being the main deter- minant of force feedback. However, haptic information is nonlinear and more factors could influence haptic feedback recreation than just motion parameters. In recent years, scientists have paid much attention to employing AI technologies with haptics. AI approach is data-driven, and a thorough understanding of the data is critical to its performance. As a result, a detailed statistical analysis is required to discover patterns and relationships within the features and identify significant features as utilizing all the factors affecting haptic object reconstruc- tion will cost higher computational time and resources. Thus, this study focused on statistically analyzing data extracted through a Disturbance Observer (DOB) and Reaction Force Observer (RFOB) based sensorless technique and selecting the significant features to reconstruct an object in a haptic dimension precisely. Furthermore, a mathematical notation was introduced to define a feature and the feature matrix was derived using the notation. Finally, the performance of an AI algorithm was then compared using different feature inputs to the AI model to understand the effect of the feature matrix.