Literature DB >> 18713689

A comparison of the effects of electrode implantation and targeting on pattern classification accuracy for prosthesis control.

Todd R Farrell1, Richard F Ff Weir.   

Abstract

The use of surface versus intramuscular electrodes as well as the effect of electrode targeting on pattern-recognition-based multifunctional prosthesis control was explored. Surface electrodes are touted for their ability to record activity from relatively large portions of muscle tissue. Intramuscular electromyograms (EMGs) can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk. However, little work has been done to compare the two. Additionally, while previous investigations have either targeted electrodes to specific muscles or used untargeted (symmetric) electrode arrays, no work has compared these approaches to determine if one is superior. The classification accuracies of pattern-recognition-based classifiers utilizing surface and intramuscular as well as targeted and untargeted electrodes were compared across 11 subjects. A repeated-measures analysis of variance revealed that when only EMG amplitude information was used from all available EMG channels, the targeted surface, targeted intramuscular, and untargeted surface electrodes produced similar classification accuracies while the untargeted intramuscular electrodes produced significantly lower accuracies. However, no statistical differences were observed between any of the electrode conditions when additional features were extracted from the EMG signal. It was concluded that the choice of electrode should be driven by clinical factors, such as signal robustness/stability, cost, etc., instead of by classification accuracy.

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Mesh:

Year:  2008        PMID: 18713689      PMCID: PMC3153447          DOI: 10.1109/TBME.2008.923917

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  43 in total

1.  On automatic identification of upper-limb movements using small-sized training sets of EMG signals.

Authors:  S Micera; A M Sabatini; P Dario
Journal:  Med Eng Phys       Date:  2000-10       Impact factor: 2.242

2.  Surface myoelectric signal classification for prostheses control.

Authors:  Y Al-Assaf; H Al-Nashash
Journal:  J Med Eng Technol       Date:  2005 Sep-Oct

3.  Myoelectric control of a computer animated hand: a new concept based on the combined use of a tree-structured artificial neural network and a data glove.

Authors:  F Sebelius; L Eriksson; C Balkenius; T Laurell
Journal:  J Med Eng Technol       Date:  2006 Jan-Feb

4.  An enhanced feature extraction algorithm for EMG pattern classification.

Authors:  S P Lee; J S Kim; S H Park
Journal:  IEEE Trans Rehabil Eng       Date:  1996-12

5.  Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model.

Authors:  Y Koike; M Kawato
Journal:  Biol Cybern       Date:  1995-09       Impact factor: 2.086

6.  Identification of lower arm motions using the EMG signals of shoulder muscles.

Authors:  A Latwesen; P E Patterson
Journal:  Med Eng Phys       Date:  1994-03       Impact factor: 2.242

7.  Two-channel enhancement of a multifunction control system.

Authors:  U Kuruganti; B Hudgins; R N Scott
Journal:  IEEE Trans Biomed Eng       Date:  1995-01       Impact factor: 4.538

8.  Evaluation of a multifunctional hand prosthesis system using EMG controlled animation.

Authors:  M Yamada; N Niwa; A Uchiyama
Journal:  IEEE Trans Biomed Eng       Date:  1983-11       Impact factor: 4.538

9.  Experience with Swedish multifunctional prosthetic hands controlled by pattern recognition of multiple myoelectric signals.

Authors:  C Almström; P Herberts; L Körner
Journal:  Int Orthop       Date:  1981       Impact factor: 3.075

10.  Surface versus intramuscular electrodes for electromyography of superficial and deep muscles.

Authors:  J Perry; C S Easterday; D J Antonelli
Journal:  Phys Ther       Date:  1981-01
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  28 in total

1.  Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration.

Authors:  Aaron J Young; Levi J Hargrove; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2011-11-29       Impact factor: 4.538

2.  Determining delay created by multifunctional prosthesis controllers.

Authors:  Todd R Farrell
Journal:  J Rehabil Res Dev       Date:  2011

3.  Electromyogram-based neural network control of transhumeral prostheses.

Authors:  Christopher L Pulliam; Joris M Lambrecht; Robert F Kirsch
Journal:  J Rehabil Res Dev       Date:  2011

4.  Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors.

Authors:  Sang Wook Lee; Kristin M Wilson; Blair A Lock; Derek G Kamper
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2010-09-27       Impact factor: 3.802

5.  The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift.

Authors:  Aaron J Young; Levi J Hargrove; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2011-06-09       Impact factor: 4.538

6.  Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist/hand motion classification.

Authors:  Lauren H Smith; Levi J Hargrove
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2013

7.  An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning.

Authors:  Geng Yang; Jia Deng; Gaoyang Pang; Hao Zhang; Jiayi Li; Bin Deng; Zhibo Pang; Juan Xu; Mingzhe Jiang; Pasi Liljeberg; Haibo Xie; Huayong Yang
Journal:  IEEE J Transl Eng Health Med       Date:  2018-05-08       Impact factor: 3.316

8.  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

9.  Extrinsic finger and thumb muscles command a virtual hand to allow individual finger and grasp control.

Authors:  J Alexander Birdwell; Levi J Hargrove; Richard F ff Weir; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2014-07-31       Impact factor: 4.538

10.  A pneumatically powered knee-ankle-foot orthosis (KAFO) with myoelectric activation and inhibition.

Authors:  Gregory S Sawicki; Daniel P Ferris
Journal:  J Neuroeng Rehabil       Date:  2009-06-23       Impact factor: 4.262

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