Literature DB >> 21938652

Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

Erik Scheme1, Kevin Englehart.   

Abstract

Using electromyogram (EMG) signals to control upper-limb prostheses is an important clinical option, offering a person with amputation autonomy of control by contracting residual muscles. The dexterity with which one may control a prosthesis has progressed very little, especially when controlling multiple degrees of freedom. Using pattern recognition to discriminate multiple degrees of freedom has shown great promise in the research literature, but it has yet to transition to a clinically viable option. This article describes the pertinent issues and best practices in EMG pattern recognition, identifies the major challenges in deploying robust control, and advocates research directions that may have an effect in the near future.

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Mesh:

Year:  2011        PMID: 21938652     DOI: 10.1682/jrrd.2010.09.0177

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


  130 in total

1.  Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands.

Authors:  Jacob L Segil; Richard F Weir
Journal:  J Rehabil Res Dev       Date:  2015

2.  Functional Assessment of a Myoelectric Postural Controller and Multi-Functional Prosthetic Hand by Persons With Trans-Radial Limb Loss.

Authors:  Jacob L Segil; Stephen A Huddle; Richard F Ff Weir
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2016-06-30       Impact factor: 3.802

3.  Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands.

Authors:  Jacob L Segil; Marco Controzzi; Richard F ff Weir; Christian Cipriani
Journal:  J Rehabil Res Dev       Date:  2014

Review 4.  Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration.

Authors:  Dapeng Yang; Yikun Gu; Nitish V Thakor; Hong Liu
Journal:  Exp Brain Res       Date:  2018-11-30       Impact factor: 1.972

5.  Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.

Authors:  Yanzheng Lu; Hong Wang; Fo Hu; Bin Zhou; Hailong Xi
Journal:  Med Biol Eng Comput       Date:  2021-03-21       Impact factor: 2.602

6.  Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure.

Authors:  Youngjin Na; Sangjoon J Kim; Sungho Jo; Jung Kim
Journal:  Med Biol Eng Comput       Date:  2017-01-04       Impact factor: 2.602

7.  Effect of arm position on the prediction of kinematics from EMG in amputees.

Authors:  Ning Jiang; Silvia Muceli; Bernhard Graimann; Dario Farina
Journal:  Med Biol Eng Comput       Date:  2012-10-23       Impact factor: 2.602

8.  A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis.

Authors:  Todd A Kuiken; Laura A Miller; Kristi Turner; Levi J Hargrove
Journal:  IEEE J Transl Eng Health Med       Date:  2016-11-22       Impact factor: 3.316

9.  A Training Strategy for Learning Pattern Recognition Control for Myoelectric Prostheses.

Authors:  Michael A Powell; Nitish V Thakor
Journal:  J Prosthet Orthot       Date:  2013-01-01

10.  Classification of simultaneous movements using surface EMG pattern recognition.

Authors:  Aaron J Young; Lauren H Smith; Elliott J Rouse; Levi J Hargrove
Journal:  IEEE Trans Biomed Eng       Date:  2012-12-10       Impact factor: 4.538

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