Literature DB >> 28624687

Estimation of distal arm joint angles from EMG and shoulder orientation for transhumeral prostheses.

Aadeel Akhtar1, Navid Aghasadeghi2, Levi Hargrove3, Timothy Bretl4.   

Abstract

In this paper, we quantify the extent to which shoulder orientation, upper-arm electromyography (EMG), and forearm EMG are predictors of distal arm joint angles during reaching in eight subjects without disability as well as three subjects with a unilateral transhumeral amputation and targeted reinnervation. Prior studies have shown that shoulder orientation and upper-arm EMG, taken separately, are predictors of both elbow flexion/extension and forearm pronation/supination. We show that, for eight subjects without disability, shoulder orientation and upper-arm EMG together are a significantly better predictor of both elbow flexion/extension during unilateral (R2=0.72) and mirrored bilateral (R2=0.72) reaches and of forearm pronation/supination during unilateral (R2=0.77) and mirrored bilateral (R2=0.70) reaches. We also show that adding forearm EMG further improves the prediction of forearm pronation/supination during unilateral (R2=0.82) and mirrored bilateral (R2=0.75) reaches. In principle, these results provide the basis for choosing inputs for control of transhumeral prostheses, both by subjects with targeted motor reinnervation (when forearm EMG is available) and by subjects without target motor reinnervation (when forearm EMG is not available). In particular, we confirm that shoulder orientation and upper-arm EMG together best predict elbow flexion/extension (R2=0.72) for three subjects with unilateral transhumeral amputations and targeted motor reinnervation. However, shoulder orientation alone best predicts forearm pronation/supination (R2=0.88) for these subjects, a contradictory result that merits further study.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Electromyography; Locally-weighted projection regression; Neural network; Reaching; Shoulder orientation; Targeted motor reinnervation

Mesh:

Year:  2017        PMID: 28624687      PMCID: PMC5546417          DOI: 10.1016/j.jelekin.2017.06.001

Source DB:  PubMed          Journal:  J Electromyogr Kinesiol        ISSN: 1050-6411            Impact factor:   2.368


  17 in total

1.  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

2.  Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom.

Authors:  Silvia Muceli; Dario Farina
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-12-13       Impact factor: 3.802

3.  Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training.

Authors:  Johnny L G Nielsen; Steffen Holmgaard; Ning Jiang; Kevin B Englehart; Dario Farina; Phil A Parker
Journal:  IEEE Trans Biomed Eng       Date:  2010-08-19       Impact factor: 4.538

4.  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

5.  Decoding a new neural machine interface for control of artificial limbs.

Authors:  Ping Zhou; Madeleine M Lowery; Kevin B Englehart; He Huang; Guanglin Li; Levi Hargrove; Julius P A Dewald; Todd A Kuiken
Journal:  J Neurophysiol       Date:  2007-08-29       Impact factor: 2.714

6.  Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control?

Authors:  Ning Jiang; Ivan Vujaklija; Hubertus Rehbaum; Bernhard Graimann; Dario Farina
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-10-25       Impact factor: 3.802

7.  Prediction of distal arm joint angles from EMG and shoulder orientation for prosthesis control.

Authors:  Aadeel Akhtar; Levi J Hargrove; Timothy Bretl
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

8.  EMG-based prediction of shoulder and elbow kinematics in able-bodied and spinal cord injured individuals.

Authors:  A T Au; R F Kirsch
Journal:  IEEE Trans Rehabil Eng       Date:  2000-12

9.  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

10.  Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Authors:  Dimitra Blana; Theocharis Kyriacou; Joris M Lambrecht; Edward K Chadwick
Journal:  J Electromyogr Kinesiol       Date:  2015-07-09       Impact factor: 2.368

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  2 in total

1.  Towards Control of a Transhumeral Prosthesis with EEG Signals.

Authors:  D S V Bandara; Jumpei Arata; Kazuo Kiguchi
Journal:  Bioengineering (Basel)       Date:  2018-03-22

2.  Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand.

Authors:  Sébastien Mick; Effie Segas; Lucas Dure; Christophe Halgand; Jenny Benois-Pineau; Gerald E Loeb; Daniel Cattaert; Aymar de Rugy
Journal:  J Neuroeng Rehabil       Date:  2021-01-06       Impact factor: 4.262

  2 in total

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