Literature DB >> 22287229

Evaluation of a noninvasive command scheme for upper-limb prostheses in a virtual reality reach and grasp task.

Rahul R Kaliki1, Rahman Davoodi, Gerald E Loeb.   

Abstract

C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation system or transhumeral prosthesis. Stereotyped relationships, termed "postural synergies," among the shoulder, forearm, and wrist joints emerge during goal-oriented reaching and transport movements as performed by able-bodied subjects. Thus, the posture of the shoulder can potentially be used to infer the desired posture of the elbow and forearm joints during reaching and transporting movements. We investigated how well able-bodied subjects could learn to use a noninvasive command scheme based on inferences from these postural synergies to control a simulated transhumeral prosthesis in a virtual reality task. We compared the performance of subjects using the inferential command scheme (ICS) with subjects operating the simulated prosthesis in virtual reality according to complete motion tracking of their actual arm and hand movements. Initially, subjects performed poorly with the ICS but improved rapidly with modest amounts of practice, eventually achieving performance only slightly less than subjects using complete motion tracking. Thus, inferring the desired movement of distal joints from voluntary shoulder movements appears to be an intuitive and noninvasive approach for obtaining command signals for prostheses to restore reaching and grasping functions.

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Year:  2012        PMID: 22287229     DOI: 10.1109/TBME.2012.2185494

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  7 in total

1.  A training platform for many-dimensional prosthetic devices using a virtual reality environment.

Authors:  David Putrino; Yan T Wong; Adam Weiss; Bijan Pesaran
Journal:  J Neurosci Methods       Date:  2014-04-13       Impact factor: 2.390

2.  Development of a closed-loop feedback system for real-time control of a high-dimensional Brain Machine Interface.

Authors:  David Putrino; Yan T Wong; Mariana Vigeral; Bijan Pesaran
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2012

3.  Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Authors:  Dimitra Blana; Theocharis Kyriacou; Joris M Lambrecht; Edward K Chadwick
Journal:  J Electromyogr Kinesiol       Date:  2015-07-09       Impact factor: 2.368

4.  Movement-Based Control for Upper-Limb Prosthetics: Is the Regression Technique the Key to a Robust and Accurate Control?

Authors:  Mathilde Legrand; Manelle Merad; Etienne de Montalivet; Agnès Roby-Brami; Nathanaël Jarrassé
Journal:  Front Neurorobot       Date:  2018-07-26       Impact factor: 2.650

5.  Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand.

Authors:  Sébastien Mick; Effie Segas; Lucas Dure; Christophe Halgand; Jenny Benois-Pineau; Gerald E Loeb; Daniel Cattaert; Aymar de Rugy
Journal:  J Neuroeng Rehabil       Date:  2021-01-06       Impact factor: 4.262

6.  Biological Plausibility of Arm Postures Influences the Controllability of Robotic Arm Teleoperation.

Authors:  Sébastien Mick; Arnaud Badets; Pierre-Yves Oudeyer; Daniel Cattaert; Aymar De Rugy
Journal:  Hum Factors       Date:  2020-08-18       Impact factor: 2.888

7.  A novel framework for designing a multi-DoF prosthetic wrist control using machine learning.

Authors:  Chinmay P Swami; Nicholas Lenhard; Jiyeon Kang
Journal:  Sci Rep       Date:  2021-07-22       Impact factor: 4.379

  7 in total

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