Functional analysis of grasping motion

Abstract

This paper presents a novel grasping motion analysis technique based on functional principal component analysis (fPCA). The functional analysis of grasping motion provides an effective representation of grasping motion and emphasizes motion dynamic features that are omitted by classic PCA-based approaches. The proposed approach represents, processes, and compares grasping motion trajectories in a low-dimensional space. An experiment was conducted to record grasping motion trajectories of 15 different grasp types in Cutkosky grasp taxonomy. We implemented our method for the analysis of collected grasping motion in the PCA+fPCA space, which generated a new data-driven taxonomy of the grasp types, and naturally clustered grasping motion into 5 consistent groups across 5 different subjects. The robustness of the grouping was evaluated and confirmed using a tenfold cross validation approach.