%0 Journal Article
%T Virtual Grasping: Closed-Loop Force Control Using Electrotactile Feedback
%A Nikola Jorgovanovic
%A Strahinja Dosen
%A Damir J. Djozic
%A Goran Krajoski
%A Dario Farina
%J Computational and Mathematical Methods in Medicine
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/120357
%X Closing the control loop by providing somatosensory feedback to the user of a prosthesis is a well-known, long standing challenge in the field of prosthetics. Various approaches have been investigated for feedback restoration, ranging from direct neural stimulation to noninvasive sensory substitution methods. Although there are many studies presenting closed-loop systems, only a few of them objectively evaluated the closed-loop performance, mostly using vibrotactile stimulation. Importantly, the conclusions about the utility of the feedback were partly contradictory. The goal of the current study was to systematically investigate the capability of human subjects to control grasping force in closed loop using electrotactile feedback. We have developed a realistic experimental setup for virtual grasping, which operated in real time, included a set of real life objects, as well as a graphical and dynamical model of the prosthesis. We have used the setup to test 10 healthy, able bodied subjects to investigate the role of training, feedback and feedforward control, robustness of the closed loop, and the ability of the human subjects to generalize the control to previously ¡°unseen¡± objects. Overall, the outcomes of this study are very optimistic with regard to the benefits of feedback and reveal various, practically relevant, aspects of closed-loop control. 1. Introduction Human grasping is characterized by a remarkable flexibility. Humans can easily grasp, lift, and manipulate objects of very different properties (e.g., texture, weight, and stiffness). Obviously, this process requires an advanced control of grasping forces, which is in human motor control implemented through a blend of feedforward and feedback mechanisms [1]. The former is well reflected in the paradigm of economical grasping: humans use previous sensory-motor experience to scale appropriately the grasping forces according to the expected (estimated) weight of the target object. The goal is to minimize the forces and thereby energy expenditure, and yet avoid slipping. However, this specific mechanism and also grasping as a whole can be significantly impaired when somatosensory feedback pathways are not fully functional due to a disease of the nervous system (e.g., multiple sclerosis [2], deafferented patients [3]). After an amputation of the hand, a prosthetic device can be used as a functional and morphological replacement of the lost limb. To control the artificial limb, the intention of the user can be inferred from the recorded activity of the user¡¯s muscles (myoelectric control). This
%U http://www.hindawi.com/journals/cmmm/2014/120357/