Literature DB >> 23515790

Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG.

Ernest N Kamavuako1, Erik J Scheme, Kevin B Englehart.   

Abstract

In this paper, the predictive capability of surface and untargeted intramuscular electromyography (EMG) was compared with respect to wrist-joint torque to quantify which type of measurement better represents joint torque during multiple degrees-of-freedom (DoF) movements for possible application in prosthetic control. Ten able-bodied subjects participated in the study. Surface and intramuscular EMG was recorded concurrently from the right forearm. The subjects were instructed to track continuous contraction profiles using single and combined DoF in two trials. The association between torque and EMG was assessed using an artificial neural network. Results showed a significant difference between the two types of EMG (P < 0.007) for all performance metrics: coefficient of determination (R(2)), Pearson correlation coefficient (PCC), and root mean square error (RMSE). The performance of surface EMG (R(2) = 0.93 ± 0.03; PCC = 0.98 ± 0.01; RMSE = 8.7 ± 2.1%) was found to be superior compared with intramuscular EMG (R(2) = 0.80 ± 0.07; PCC = 0.93 ± 0.03; RMSE = 14.5 ± 2.9%). The higher values of PCC compared with R(2) indicate that both methods are able to track the torque profile well but have some trouble (particularly intramuscular EMG) in estimating the exact amplitude. The possible cause for the difference, thus the low performance of intramuscular EMG, may be attributed to the very high selectivity of the recordings used in this study.

Entities:  

Keywords:  dynamic movement; intramuscular EMG; simultaneous movement; surface electromyography; torque and force estimation

Mesh:

Year:  2013        PMID: 23515790     DOI: 10.1152/jn.00086.2013

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  9 in total

1.  Real-time simultaneous and proportional myoelectric control using intramuscular EMG.

Authors:  Lauren H Smith; Todd A Kuiken; Levi J Hargrove
Journal:  J Neural Eng       Date:  2014-11-14       Impact factor: 5.379

2.  A 3-DOF hemi-constrained wrist motion/force detection device for deploying simultaneous myoelectric control.

Authors:  Wei Yang; Dapeng Yang; Yu Liu; Hong Liu
Journal:  Med Biol Eng Comput       Date:  2018-03-05       Impact factor: 2.602

3.  Myoelectric Control System and Task-Specific Characteristics Affect Voluntary Use of Simultaneous Control.

Authors:  Lauren H Smith; Todd A Kuiken; Levi J Hargrove
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2015-03-06       Impact factor: 3.802

4.  Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.

Authors:  Lauren H Smith; Todd A Kuiken; Levi J Hargrove
Journal:  IEEE Trans Biomed Eng       Date:  2015-08-20       Impact factor: 4.538

5.  Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography.

Authors:  Maram Sakr; Xianta Jiang; Carlo Menon
Journal:  Front Robot AI       Date:  2019-11-22

6.  Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals.

Authors:  Ines Chihi; Lilia Sidhom; Ernest Nlandu Kamavuako
Journal:  Biosensors (Basel)       Date:  2022-02-13

7.  A Co-driven Functional Electrical Stimulation Control Strategy by Dynamic Surface Electromyography and Joint Angle.

Authors:  Rui Xu; Xinyu Zhao; Ziyao Wang; Hengyu Zhang; Lin Meng; Dong Ming
Journal:  Front Neurosci       Date:  2022-07-08       Impact factor: 5.152

8.  Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography.

Authors:  Nebojsa Malesevic; Anders Björkman; Gert S Andersson; Christian Cipriani; Christian Antfolk
Journal:  Sensors (Basel)       Date:  2022-07-05       Impact factor: 3.847

Review 9.  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

  9 in total

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