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Estimation of Grasping Force from Features of Intramuscular EMG Signals with Mirrored Bilateral Training

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Abstract

This study investigates the use of features extracted from intramuscular electromyography (EMG) for estimating grasping force in the ipsilateral and contralateral (mirrored) hand, during bilateral grasping tasks. This is relevant since force estimation using mirror tasks is a potentially useful pathway for the clinical training of unilateral amputees. Bilateral grasping force and intramuscular EMG (wire electrodes) of the right forearm were measured in 10 able-bodied subjects. The features extracted from the EMG signal were the root mean square, the global discharge rate, the standard sample entropy, and the constraint sample entropy (CSE). The association between the EMG features and force was modeled using a first-order polynomial model, a second-order exponential model, and an artificial neural network (ANN). The accuracies of estimation of ipsilateral and mirrored grasping force were not significantly different (e.g., R 2 = 0.89 ± 0.02 for ipsilateral and 0.88 ± 0.017 for mirrored, when using CSE and the ANN). It was concluded that it is possible to use just one channel of intramuscular EMG for force estimation. This result suggests that intramuscular EMG signals may be suitable for proportional myoelectric control and that training of the association between intramuscular EMG features and force can be performed using mirror tasks, which is a needed condition for applications in unilateral amputees.

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Abbreviations

RMS:

Root mean square

GDR:

Global discharge rate

SSE:

Standard sample entropy

CSE:

Constraint sample entropy

ANN:

Artificial neural network

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Acknowledgments

This study was supported by a grant from the Danish Agency for Science, Technology and Innovation (Council for Independent Research|Technology and Production Sciences, Grant number 10-080813) (ENK) and by the ERC Advanced Research Grant DEMOVE (“Decoding the Neural Code of Human Movements for a New Generation of Man–machine Interfaces”; no.: 267888) (DF).

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Authors and Affiliations

  1. Center for Sensory-Motor Interaction (SMI), Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7 D3, 9220, Aalborg, Denmark

    Ernest Nlandu Kamavuako, Dario Farina, Ken Yoshida & Winnie Jensen

  2. Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Göttingen, Germany

    Dario Farina

  3. Biomedical Engineering Department, IUPUI, Indianapolis, IN, USA

    Ken Yoshida

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  1. Ernest Nlandu Kamavuako
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  2. Dario Farina
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  3. Ken Yoshida
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  4. Winnie Jensen
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Correspondence to Ernest Nlandu Kamavuako.

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Associate Editor Catherine Disselhorst-Klug oversaw the review of this article.

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Kamavuako, E.N., Farina, D., Yoshida, K. et al. Estimation of Grasping Force from Features of Intramuscular EMG Signals with Mirrored Bilateral Training. Ann Biomed Eng 40, 648–656 (2012). https://doi.org/10.1007/s10439-011-0438-7

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  • DOI: https://doi.org/10.1007/s10439-011-0438-7

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