Literature DB >> 24801625

Closed-loop control of grasping with a myoelectric hand prosthesis: which are the relevant feedback variables for force control?

Andrei Ninu, Strahinja Dosen, Silvia Muceli, Frank Rattay, Hans Dietl, Dario Farina.   

Abstract

In closed-loop control of grasping by hand prostheses, the feedback information sent to the user is usually the actual controlled variable, i.e., the grasp force. Although this choice is intuitive and logical, the force production is only the last step in the process of grasping. Therefore, this study evaluated the performance in controlling grasp strength using a hand prosthesis operated through a complete grasping sequence while varying the feedback variables (e.g., closing velocity, grasping force), which were provided to the user visually or through vibrotactile stimulation. The experiments were conducted on 13 volunteers who controlled the Otto Bock Sensor Hand Speed prosthesis. Results showed that vibrotactile patterns were able to replace the visual feedback. Interestingly, the experiments demonstrated that direct force feedback was not essential for the control of grasping force. The subjects were indeed able to control the grip strength, predictively, by estimating the grasping force from the prosthesis velocity of closing. Therefore, grasping without explicit force feedback is not completely blind, contrary to what is usually assumed. In our study we analyzed grasping with a specific prosthetic device, but the outcomes are also applicable for other devices, with one or more degrees-of-freedom. The necessary condition is that the electromyography (EMG) signal directly and proportionally controls the velocity/grasp force of the hand, which is a common approach among EMG controlled prosthetic devices. The results provide important indications on the design of closed-loop EMG controlled prosthetic systems.

Mesh:

Year:  2014        PMID: 24801625     DOI: 10.1109/TNSRE.2014.2318431

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  19 in total

1.  Sensory feedback by peripheral nerve stimulation improves task performance in individuals with upper limb loss using a myoelectric prosthesis.

Authors:  Matthew Schiefer; Daniel Tan; Steven M Sidek; Dustin J Tyler
Journal:  J Neural Eng       Date:  2015-12-08       Impact factor: 5.379

2.  Building an internal model of a myoelectric prosthesis via closed-loop control for consistent and routine grasping.

Authors:  Strahinja Dosen; Marko Markovic; Nicola Wille; Markus Henkel; Mario Koppe; Andrei Ninu; Cornelius Frömmel; Dario Farina
Journal:  Exp Brain Res       Date:  2015-03-25       Impact factor: 1.972

3.  Joint-based velocity feedback to virtual limb dynamic perturbations.

Authors:  Eric J Earley; Kyle J Kaveny; Reva E Johnson; Levi J Hargrove; Jon W Sensinger
Journal:  IEEE Int Conf Rehabil Robot       Date:  2017-07

Review 4.  The science and engineering behind sensitized brain-controlled bionic hands.

Authors:  Chethan Pandarinath; Sliman J Bensmaia
Journal:  Physiol Rev       Date:  2021-09-20       Impact factor: 37.312

5.  A Multi-User Transradial Functional-Test Socket for Validation of New Myoelectric Prosthetic Control Strategies.

Authors:  Taylor C Hansen; Abigail R Citterman; Eric S Stone; Troy N Tully; Christopher M Baschuk; Christopher C Duncan; Jacob A George
Journal:  Front Neurorobot       Date:  2022-06-17       Impact factor: 3.493

6.  EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis.

Authors:  Strahinja Dosen; Marko Markovic; Kelef Somer; Bernhard Graimann; Dario Farina
Journal:  J Neuroeng Rehabil       Date:  2015-06-19       Impact factor: 4.262

7.  Learning to use a body-powered prosthesis: changes in functionality and kinematics.

Authors:  Laura H B Huinink; Hanneke Bouwsema; Dick H Plettenburg; Corry K van der Sluis; Raoul M Bongers
Journal:  J Neuroeng Rehabil       Date:  2016-10-07       Impact factor: 4.262

8.  Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels.

Authors:  Alessandro Marco De Nunzio; Strahinja Dosen; Sabrina Lemling; Marko Markovic; Meike Annika Schweisfurth; Nan Ge; Bernhard Graimann; Deborah Falla; Dario Farina
Journal:  Exp Brain Res       Date:  2017-05-26       Impact factor: 1.972

9.  An exploration of grip force regulation with a low-impedance myoelectric prosthesis featuring referred haptic feedback.

Authors:  Jeremy D Brown; Andrew Paek; Mashaal Syed; Marcia K O'Malley; Patricia A Shewokis; Jose L Contreras-Vidal; Alicia J Davis; R Brent Gillespie
Journal:  J Neuroeng Rehabil       Date:  2015-11-25       Impact factor: 4.262

10.  The clinical relevance of advanced artificial feedback in the control of a multi-functional myoelectric prosthesis.

Authors:  Marko Markovic; Meike A Schweisfurth; Leonard F Engels; Tashina Bentz; Daniela Wüstefeld; Dario Farina; Strahinja Dosen
Journal:  J Neuroeng Rehabil       Date:  2018-03-27       Impact factor: 4.262

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