Grasp taxonomy based on force distribution

Abstract

Human grasp has been studied extensively and many taxonomies have been developed to classify different grasp types. In this work, we collect a comprehensive list of grasp types and investigate the force distribution patterns among them. We conduct a human study and collect force data in various grasp types. Data collection is performed by utilizing a data glove that has seventeen force sensors. We analyze the measured forces and identify different patterns in force distribution. Employing voting technique over various clustering methods, we propose a robust clustering for the force patterns, namely the grasp taxonomy in force domain. The proposed grasp taxonomy can be exploited in designing robotic prosthetic hands, designing grasp controllers and action recognition in human-robot interaction.