Literature DB >> 25142151

Internal models of upper limb prosthesis users when grasping and lifting a fragile object with their prosthetic limb.

Peter S Lum1, Iian Black, Rahsaan J Holley, Jessica Barth, Alexander W Dromerick.   

Abstract

Internal models allow unimpaired individuals to appropriately scale grip force when grasping and lifting familiar objects. In prosthesis users, the internal model must adapt to the characteristics of the prosthetic devices and reduced sensory feedback. We studied the internal models of 11 amputees and eight unimpaired controls when grasping and lifting a fragile object. When the object was modified from a rigid to fragile state, both subject groups adapted appropriately by significantly reducing grasp force on the first trial with the fragile object compared to the rigid object (p < 0.020). There was a wide range of performance skill illustrated by amputee subjects when lifting the fragile object in 10 repeated trials. One subject, using a voluntary close device, never broke the object, four subjects broke the fragile device on every attempt and seven others failed on their initial attempts, but improved over the repeated trials. Amputees decreased their grip forces 51 ± 7 % from the first to the last trial (p < 0.001), indicating a practice effect. However, amputees used much higher levels of force than controls throughout the testing (p < 0.015). Amputees with better performance on the Box and Blocks test used lower grip force levels (p = 0.006) and had more successful lifts of the fragile object (p = 0.002). In summary, amputees do employ internal models when picking up objects; however, the accuracy of these models is poor and grip force modulation is significantly impaired. Further studies could examine the alternative sensory modalities and training parameters that best promote internal model formation.

Mesh:

Year:  2014        PMID: 25142151     DOI: 10.1007/s00221-014-4071-1

Source DB:  PubMed          Journal:  Exp Brain Res        ISSN: 0014-4819            Impact factor:   1.972


  50 in total

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Authors:  M Rijntjes; C Dettmers; C Büchel; S Kiebel; R S Frackowiak; C Weiller
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Review 2.  Internal models for motor control and trajectory planning.

Authors:  M Kawato
Journal:  Curr Opin Neurobiol       Date:  1999-12       Impact factor: 6.627

Review 3.  Upper limb prosthesis use and abandonment: a survey of the last 25 years.

Authors:  Elaine A Biddiss; Tom T Chau
Journal:  Prosthet Orthot Int       Date:  2007-09       Impact factor: 1.895

4.  Adaptive filtering of the electromyographic signal for prosthetic control and force estimation.

Authors:  E Park; S G Meek
Journal:  IEEE Trans Biomed Eng       Date:  1995-10       Impact factor: 4.538

5.  Memory representations underlying motor commands used during manipulation of common and novel objects.

Authors:  A M Gordon; G Westling; K J Cole; R S Johansson
Journal:  J Neurophysiol       Date:  1993-06       Impact factor: 2.714

6.  Feedforward control strategies of subjects with transradial amputation in planar reaching.

Authors:  Anthony J Metzger; Alexander W Dromerick; Christopher N Schabowsky; Rahsaan J Holley; Brian Monroe; Peter S Lum
Journal:  J Rehabil Res Dev       Date:  2010

7.  Evaluation of prosthetic usage in upper limb amputees.

Authors:  I Dudkiewicz; R Gabrielov; I Seiv-Ner; G Zelig; M Heim
Journal:  Disabil Rehabil       Date:  2004-01-07       Impact factor: 3.033

8.  Training with an upper-limb prosthetic simulator to enhance transfer of skill across limbs.

Authors:  Douglas L Weeks; Stephen A Wallace; David I Anderson
Journal:  Arch Phys Med Rehabil       Date:  2003-03       Impact factor: 3.966

9.  Bio-inspired sensorization of a biomechatronic robot hand for the grasp-and-lift task.

Authors:  B B Edin; L Ascari; L Beccai; S Roccella; J-J Cabibihan; M C Carrozza
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10.  Changes in performance over time while learning to use a myoelectric prosthesis.

Authors:  Hanneke Bouwsema; Corry K van der Sluis; Raoul M Bongers
Journal:  J Neuroeng Rehabil       Date:  2014-02-25       Impact factor: 4.262

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  14 in total

1.  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

2.  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
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3.  High Cable Forces Deteriorate Pinch Force Control in Voluntary-Closing Body-Powered Prostheses.

Authors:  Mona Hichert; David A Abbink; Peter J Kyberd; Dick H Plettenburg
Journal:  PLoS One       Date:  2017-01-18       Impact factor: 3.240

4.  Case-study of a user-driven prosthetic arm design: bionic hand versus customized body-powered technology in a highly demanding work environment.

Authors:  Wolf Schweitzer; Michael J Thali; David Egger
Journal:  J Neuroeng Rehabil       Date:  2018-01-03       Impact factor: 4.262

5.  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

6.  Ipsilateral Scapular Cutaneous Anchor System: An alternative for the harness in body-powered upper-limb prostheses.

Authors:  Mona Hichert; Dick H Plettenburg
Journal:  Prosthet Orthot Int       Date:  2017-03-20       Impact factor: 1.895

7.  Improving Fine Control of Grasping Force during Hand-Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand.

Authors:  Qiushi Fu; Marco Santello
Journal:  Front Neurorobot       Date:  2018-01-10       Impact factor: 2.650

8.  Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control.

Authors:  Ahmed W Shehata; Erik J Scheme; Jonathon W Sensinger
Journal:  Sci Rep       Date:  2018-06-04       Impact factor: 4.379

9.  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

10.  Neural feedback strategies to improve grasping coordination in neuromusculoskeletal prostheses.

Authors:  Enzo Mastinu; Leonard F Engels; Francesco Clemente; Mariama Dione; Paolo Sassu; Oskar Aszmann; Rickard Brånemark; Bo Håkansson; Marco Controzzi; Johan Wessberg; Christian Cipriani; Max Ortiz-Catalan
Journal:  Sci Rep       Date:  2020-07-16       Impact factor: 4.379

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