Literature DB >> 28983183

Multi-position Training Improves Robustness of Pattern Recognition and Reduces Limb-Position Effect in Prosthetic Control.

Robert J Beaulieu1, Matthew R Masters1, Joseph Betthauser1, Ryan J Smith1, Rahul Kaliki2, Nitish V Thakor1, Alcimar B Soares3.   

Abstract

Entities:  

Year:  2017        PMID： 28983183      PMCID： PMC5624523          DOI： 10.1097/JPO.0000000000000121

Source DB:  PubMed          Journal:  J Prosthet Orthot        ISSN： 1040-8800


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  19 in total

1.  Improving myoelectric pattern recognition positional robustness using advanced training protocols.

Authors:  E Scheme; K Biron; K Englehart
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

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

Authors:  Erik Scheme; Kevin Englehart
Journal:  J Rehabil Res Dev       Date:  2011

3.  Examining the adverse effects of limb position on pattern recognition based myoelectric control.

Authors:  E Scheme; A Fougner; Ø Stavdahl; A C Chan; K Englehart
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2010

4.  A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment.

Authors:  L Hargrove; Y Losier; B Lock; K Englehart; B Hudgins
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2007

5.  Selective classification for improved robustness of myoelectric control under nonideal conditions.

Authors:  Erik J Scheme; Kevin B Englehart; Bernard S Hudgins
Journal:  IEEE Trans Biomed Eng       Date:  2011-02-10       Impact factor: 4.538

6.  Resolving the limb position effect in myoelectric pattern recognition.

Authors:  Anders Fougner; Erik Scheme; Adrian D C Chan; Kevin Englehart; Oyvind Stavdahl
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-08-15       Impact factor: 3.802

7.  Adaptive common average filtering for myocontrol applications.

Authors:  Hubertus Rehbaum; Dario Farina
Journal:  Med Biol Eng Comput       Date:  2014-11-12       Impact factor: 2.602

Review 8.  The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges.

Authors:  Dario Farina; Ning Jiang; Hubertus Rehbaum; Aleš Holobar; Bernhard Graimann; Hans Dietl; Oskar C Aszmann
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-02-11       Impact factor: 3.802

9.  A Multi-Class Proportional Myocontrol Algorithm for Upper Limb Prosthesis Control: Validation in Real-Life Scenarios on Amputees.

Authors:  Sebastian Amsuess; Peter Goebel; Bernhard Graimann; Dario Farina
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-10-03       Impact factor: 3.802

10.  High-density surface EMG maps from upper-arm and forearm muscles.

Authors:  Monica Rojas-Martínez; Miguel A Mañanas; Joan F Alonso
Journal:  J Neuroeng Rehabil       Date:  2012-12-10       Impact factor: 4.262
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  3 in total

1.  Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

Authors:  Joseph L Betthauser; Christopher L Hunt; Luke E Osborn; Matthew R Masters; Gyorgy Levay; Rahul R Kaliki; Nitish V Thakor
Journal:  IEEE Trans Biomed Eng       Date:  2017-06-23       Impact factor: 4.538

2.  Understanding Limb Position and External Load Effects on Real-Time Pattern Recognition Control in Amputees.

Authors:  Yuni Teh; Levi J Hargrove
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-05-11       Impact factor: 3.802

3.  Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.

Authors:  Evan Campbell; Angkoon Phinyomark; Erik Scheme
Journal:  Sensors (Basel)       Date:  2020-03-13       Impact factor: 3.576
  3 in total

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