Literature DB >> 28384495

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes.

Edward A Clancy1, Carlos Martinez-Luna2, Marek Wartenberg3, Chenyun Dai4, Todd R Farrell2.   

Abstract

Surface electromyogram-controlled powered hand/wrist prostheses return partial upper-limb function to limb-absent persons. Typically, one degree of freedom (DoF) is controlled at a time, with mode switching between DoFs. Recent research has explored using large-channel EMG systems to provide simultaneous, independent and proportional (SIP) control of two joints-but such systems are not practical in current commercial prostheses. Thus, we investigated site selection of a minimum number of conventional EMG electrodes in an EMG-force task, targeting four sites for a two DoF controller. In a laboratory experiment with 10 able-bodied subjects and three limb-absent subjects, 16 electrodes were placed about the proximal forearm. Subjects produced 1-DoF and 2-DoF slowly force-varying contractions up to 30% maximum voluntary contraction (MVC). EMG standard deviation was related to forces via regularized regression. Backward stepwise selection was used to retain those progressively fewer electrodes that exhibited minimum error. For 1-DoF models using two retained electrodes (which mimics the current state of the art), subjects had average RMS errors of (depending on the DoF): 7.1-9.5% MVC for able-bodied and 13.7-17.1% MVC for limb-absent subjects. For 2-DoF models, subjects using four electrodes had errors on 1-DoF trials of 6.7-8.5% MVC for able-bodied and 11.9-14.0% MVC for limb-absent; and errors on 2-DoF trials of 9.9-11.2% MVC for able-bodied and 15.8-16.7% MVC for limb-absent subjects. For each model, retaining more electrodes did not statistically improve performance. The able-bodied results suggest that backward selection is a viable method for minimum error selection of as few as four electrode sites for these EMG-force tasks. Performance evaluation in a prosthesis control task is a necessary and logical next step for this site selection method.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  EMG; EMG signal processing; EMG-force; Electromyogram; Myoelectric control; Prosthesis; Prosthesis control

Mesh:

Year:  2017        PMID: 28384495      PMCID: PMC5481845          DOI: 10.1016/j.jelekin.2017.03.004

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


  42 in total

1.  Adaptive whitening of the electromyogram to improve amplitude estimation.

Authors:  E A Clancy; K A Farry
Journal:  IEEE Trans Biomed Eng       Date:  2000-06       Impact factor: 4.538

2.  A robust, real-time control scheme for multifunction myoelectric control.

Authors:  Kevin Englehart; Bernard Hudgins
Journal:  IEEE Trans Biomed Eng       Date:  2003-07       Impact factor: 4.538

3.  EMG-force modeling using parallel cascade identification.

Authors:  Javad Hashemi; Evelyn Morin; Parvin Mousavi; Katherine Mountjoy; Keyvan Hashtrudi-Zaad
Journal:  J Electromyogr Kinesiol       Date:  2012-01-28       Impact factor: 2.368

4.  Identification of constant-posture EMG-torque relationship about the elbow using nonlinear dynamic models.

Authors:  Edward A Clancy; Lukai Liu; Pu Liu; Daniel V Zandt Moyer
Journal:  IEEE Trans Biomed Eng       Date:  2011-10-03       Impact factor: 4.538

5.  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

6.  Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control.

Authors:  Max Ortiz-Catalan; Faezeh Rouhani; Rickard Branemark; Bo Hakansson
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2015-08

7.  User training for pattern recognition-based myoelectric prostheses: improving phantom limb movement consistency and distinguishability.

Authors:  Michael A Powell; Rahul R Kaliki; Nitish V Thakor
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-10-07       Impact factor: 3.802

8.  Context-Dependent Upper Limb Prosthesis Control for Natural and Robust Use.

Authors:  Sebastian Amsuess; Ivan Vujaklija; Peter Goebel; Aidan D Roche; Bernhard Graimann; Oskar C Aszmann; Dario Farina
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2015-07-09       Impact factor: 3.802

9.  Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

Authors:  J M Hahne; F Biessmann; N Jiang; H Rehbaum; D Farina; F C Meinecke; K-R Muller; L C Parra
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-03       Impact factor: 3.802

10.  Enhanced dynamic EMG-force estimation through calibration and PCI modeling.

Authors:  Javad Hashemi; Evelyn Morin; Parvin Mousavi; Keyvan Hashtrudi-Zaad
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-05-21       Impact factor: 3.802

View more
  4 in total

1.  Two degrees of freedom, dynamic, hand-wrist EMG-force using a minimum number of electrodes.

Authors:  Chenyun Dai; Ziling Zhu; Carlos Martinez-Luna; Thane R Hunt; Todd R Farrell; Edward A Clancy
Journal:  J Electromyogr Kinesiol       Date:  2019-04-16       Impact factor: 2.368

2.  EMG-Force and EMG-Target Models During Force-Varying Bilateral Hand-Wrist Contraction in Able-Bodied and Limb-Absent Subjects.

Authors:  Ziling Zhu; Carlos Martinez-Luna; Jianan Li; Benjamin E McDonald; Chenyun Dai; Xinming Huang; Todd R Farrell; Edward A Clancy
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2021-01-28       Impact factor: 3.802

3.  Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects.

Authors:  Ziling Zhu; Jianan Li; William J Boyd; Carlos Martinez-Luna; Chenyun Dai; Haopeng Wang; He Wang; Xinming Huang; Todd R Farrell; Edward A Clancy
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2022-04-11       Impact factor: 4.528

Review 4.  Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.

Authors:  Reed D Gurchiek; Nick Cheney; Ryan S McGinnis
Journal:  Sensors (Basel)       Date:  2019-11-28       Impact factor: 3.576

  4 in total

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