Literature DB >> 24013916

Motor control and learning with lower-limb myoelectric control in amputees.

Ramses E Alcaide-Aguirre1, David C Morgenroth, Daniel P Ferris.   

Abstract

Advances in robotic technology have recently enabled the development of powered lower-limb prosthetic limbs. A major hurdle in developing commercially successful powered prostheses is the control interface. Myoelectric signals are one way for prosthetic users to provide feedforward volitional control of prosthesis mechanics. The goal of this study was to assess motor learning in people with lower-limb amputation using proportional myoelectric control from residual-limb muscles. We examined individuals with transtibial amputation and nondisabled controls performing tracking tasks of a virtual object. We assessed how quickly the individuals with amputation improved their performance and whether years since amputation correlated with performance. At the beginning of training, subjects with amputation performed much worse than control subjects. By the end of a short training period, tracking error did not significantly differ between subjects with amputation and nondisabled subjects. Initial but not final performance correlated significantly with time since amputation. This study demonstrates that although subjects with amputation may initially have poor volitional control of their residual lower-limb muscles, training can substantially improve their volitional control. These findings are encouraging for the future use of proportional myoelectric control of powered lower-limb prostheses.

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Year:  2013        PMID: 24013916     DOI: 10.1682/jrrd.2012.06.0115

Source DB:  PubMed          Journal:  J Rehabil Res Dev        ISSN: 0748-7711


  7 in total

1.  Stand-Up, Squat, Lunge, and Walk With a Robotic Knee and Ankle Prosthesis Under Shared Neural Control.

Authors:  Grace Hunt; Sarah Hood; Tommaso Lenzi
Journal:  IEEE Open J Eng Med Biol       Date:  2021-08-11

2.  Control of Leg Movements Driven by EMG Activity of Shoulder Muscles.

Authors:  Valentina La Scaleia; Francesca Sylos-Labini; Thomas Hoellinger; Letian Wang; Guy Cheron; Francesco Lacquaniti; Yuri P Ivanenko
Journal:  Front Hum Neurosci       Date:  2014-10-20       Impact factor: 3.169

3.  The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control.

Authors:  Alix Chadwell; Laurence Kenney; Sibylle Thies; Adam Galpin; John Head
Journal:  Front Neurorobot       Date:  2016-08-22       Impact factor: 2.650

Review 4.  Cognitive Representation of Human Action: Theory, Applications, and Perspectives.

Authors:  Christian Seegelke; Thomas Schack
Journal:  Front Public Health       Date:  2016-02-18

5.  Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses.

Authors:  A Chadwell; L Kenney; S Thies; J Head; A Galpin; R Baker
Journal:  Sci Rep       Date:  2021-02-08       Impact factor: 4.379

6.  Direction of attentional focus in prosthetic training: Current practice and potential for improving motor learning in individuals with lower limb loss.

Authors:  Szu-Ping Lee; Alexander Bonczyk; Maria Katrina Dimapilis; Sarah Partridge; Samantha Ruiz; Lung-Chang Chien; Andrew Sawers
Journal:  PLoS One       Date:  2022-07-07       Impact factor: 3.752

Review 7.  Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions.

Authors:  Aaron Fleming; Nicole Stafford; Stephanie Huang; Xiaogang Hu; Daniel P Ferris; He Helen Huang
Journal:  J Neural Eng       Date:  2021-07-27       Impact factor: 5.379

  7 in total

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