Generalizability of Hand Kinematic Synergies derived using Independent Component Analysis

Abstract

In this paper, hand synergies were derived using independent component analysis (ICA) and compared against synergies derived from our previous methods using principal component analysis (PCA). For ICA, we used two algorithms — Infomax and entropy bound minimization (EBM). For all the methods, the synergies were extracted from rapid hand grasps. The extracted synergies were then tested for generalizability in reconstructing natural hand grasps and American Sign Language (ASL) postures that were different from rapid grasps. The results indicate that the synergies derived from ICA were able to generalize only marginally better when compared to those from PCA. Among the two ICA methods, Infomax performed slightly better in yielding lower reconstruction error while EBM performed better in sparse selection of synergies. The implications and future scope were discussed.