Imperial College London
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ProfessorDarioFarina

Faculty of Engineering, Department of Bioengineering

Chair in Neurorehabilitation Engineering
 
 
 
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Contact

 

+44 (0)20 7594 1387d.farina Website

 
 
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Location

 

RSM 4.15Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kapelner:2018:10.1109/TNSRE.2017.2766360,
author = {Kapelner, T and Negro, F and Aszmann, OC and Farina, D},
doi = {10.1109/TNSRE.2017.2766360},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
pages = {244--251},
title = {Decoding Motor Unit Activity from Forearm Muscles: Perspectives for Myoelectric Control},
url = {http://dx.doi.org/10.1109/TNSRE.2017.2766360},
volume = {26},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - © 2017 IEEE. We prove the feasibility of decomposing high density surface EMG signals from forearm muscles in non-isometric wrist motor tasks of normally limbed and limb-deficient individuals with the perspective of using the decoded neural information for prosthesis control. For this purpose, we recorded surface EMG signals during motions of three degrees of freedom of the wrist in seven normally limbed subjects and two patients with limb deficiency. The signals were decomposed into individual motor unit activity with a convolutive blind source separation algorithm. On average, for each subject, 16 ± 7 motor units were identified per motor task. The discharge timings of these motor units were estimated with an accuracy > 85%. Moreover, the activity of 6 ± 5 motor units per motor task was consistently detected in all repetitions of the same task. The joint angle at which motor units were first identified was 62.5 ± 26.4% of the range of motion, indicating a prevalence in the identification of high threshold motor units. These findings prove the feasibility of accurate identification of the neural drive to muscles in contractions relevant for myoelectric control, allowing the development of a new generation of myocontrol methods based on motor unit spike trains.
AU  - Kapelner,T
AU  - Negro,F
AU  - Aszmann,OC
AU  - Farina,D
DO  - 10.1109/TNSRE.2017.2766360
EP  - 251
PY  - 2018///
SN  - 1534-4320
SP  - 244
TI  - Decoding Motor Unit Activity from Forearm Muscles: Perspectives for Myoelectric Control
T2  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
UR  - http://dx.doi.org/10.1109/TNSRE.2017.2766360
VL  - 26
ER  -
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