Force Exertion Affects Grasp Classification Using Force Myography

Abstract

This paper describes a study that explores the force exertion effect on the classification of grasps using a force myography (FMG) technology. Nine participants were recruited to the study; each performed a set of 16 different grasps from a grasp taxonomy using eight different levels of force, respectively. Their wrist muscle pressure was recorded using an array of 16 force sensing resistors. A linear discriminant analysis model was trained by grasps at a single force level using the natural grasping force to classify grasps generated by eight different levels of force. The results show that the grasping force significantly affects the accuracy of grasp classification such that a grasping force closer to the natural force achieves a higher accuracy. A still acceptable classification performance can be achieved for approximately half of the natural grasping force. The findings of this study help the understanding of how force exertion can affect grasp recognition using FMG. Knowledge gained from this study will provide guidance for the implementation of gesture control interfaces in terms of grasping force variations.