RT Journal Article
T1 Short-and long-term learning of feedforward control of a myoelectric prosthesis with sensory feedback by amputees
A1 Štrbac, Matija
A1 Isaković, Milica
A1 Belić, Minja
A1 Popović, Igor
A1 Simanić, Igor
A1 Farina, Dario
A1 Keller, Thierry
A1 Došen, Strahinja
AB Human motor control relies on a combination of feedback and feedforward strategies. The aim of this study was to longitudinally investigate artificial somatosensory feedback and feedforward control in the context of grasping with myoelectric prosthesis. Nine amputee subjects performed routine grasping trials, with the aim to produce four levels of force during four blocks of 60 trials across five days. The electrotactile force feedback was provided in the second and third block usingmultipad electrode and spatial coding. The first baseline and last validation block (open-loop control) evaluated the effects of long-(across sessions) and short-term (within session) learning, respectively.The outcomemeasureswere the absolute error between the generated and target force, and the number of force saturations. The results demonstrated that the electrotactile feedback improved the performance both within and across sessions. In the validation block, the performance did not significantly decrease and the quality of openloop control (baseline) improved across days, converging to the performance characterizing closed-loop control. This paper provides important insights into the feedback and feedforwardprocessesin prosthesiscontrol, contributing to the better understanding of the role and design of feedback in prosthetic systems.
SN 1534-4320
YR 2017
FD 2017
LK https://hdl.handle.net/11556/4542
UL https://hdl.handle.net/11556/4542
LA eng
NO Štrbac , M , Isaković , M , Belić , M , Popović , I , Simanić , I , Farina , D , Keller , T & Došen , S 2017 , ' Short-and long-term learning of feedforward control of a myoelectric prosthesis with sensory feedback by amputees ' , IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 25 , no. 11 , 7940016 , pp. 2133-2145 . https://doi.org/10.1109/TNSRE.2017.2712287
NO Publisher Copyright: © 2017 IEEE.
NO Manuscript received October 19, 2016; revised April 2, 2017; accepted May 17, 2017. Date of publication June 6, 2017; date of current version November 6, 2017. This work was supported in part by Tec-nalia Research & Innovation, Spain, in part by FIK, Spain, in part by the European Commission under the MYOSENS project (FP7-PEOPLE-2011-IAPP-286208), and in part by the Ministry of Education, Science and Technological Development of Serbia under Project 175016. (Corresponding author: Matija Štrbac.) M. Štrbac and M. Isaković are with Tecnalia Serbia Ltd., 11000 Belgrade, Serbia, and also with the School of Electrical Engineering, University of Belgrade, Belgrade 11000, Serbia (e-mail: matija.strbac@tecnalia.com; isakovic@etf.rs).
DS TECNALIA Publications
RD 28 jul 2024