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{Barsotti:2019:10.1109/LRA.2018.2885753,
author = {Barsotti, M and Dupan, S and Vujaklija, I and Doen, S and Frisoli, A and Farina, D},
doi = {10.1109/LRA.2018.2885753},
journal = {IEEE Robotics and Automation Letters},
pages = {217--223},
title = {Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications},
url = {http://dx.doi.org/10.1109/LRA.2018.2885753},
volume = {4},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - © 2018 IEEE. A hand impairment can have a profound impact on the quality of life. This has motivated the development of dexterous prosthetic and orthotic devices. However, their control with neuromuscular interfacing remains challenging. Moreover, existing myocontrol interfaces typically require an extensive calibration. We propose a minimally supervised, online myocontrol system for proportional and simultaneous finger force estimation based on ridge regression using only individual finger tasks for training. We compare the performance of this system when using two feature sets extracted from high-density electromyography (EMG) recordings: EMG linear envelope (ENV) and non-linear EMG to muscle activation mapping (ACT). Eight intact-limb participants were tested using online target reaching tasks. On average, the subjects hit 85% ± 9% and 91% ± 11% of single finger targets with ENV and ACT features, respectively. The hit rate for combined finger targets decreased to 29% ± 16% (ENV) and 53% ± 23% (ACT). The non-linear transformation (ACT) therefore improved the performance, leading to higher completion rate and more stable control, especially for the non-trained movement classes (better generalization). These results demonstrate the feasibility of proportional multiple finger control in intact subjects by regression on non-linear EMG features with a minimal training set of single finger tasks.
AU  - Barsotti,M
AU  - Dupan,S
AU  - Vujaklija,I
AU  - Doen,S
AU  - Frisoli,A
AU  - Farina,D
DO  - 10.1109/LRA.2018.2885753
EP  - 223
PY  - 2019///
SP  - 217
TI  - Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications
T2  - IEEE Robotics and Automation Letters
UR  - http://dx.doi.org/10.1109/LRA.2018.2885753
VL  - 4
ER  -
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