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

@inproceedings{Yeung:2019:10.1109/ICORR.2019.8779520,
author = {Yeung, D and Farina, D and Vujaklija, I},
doi = {10.1109/ICORR.2019.8779520},
pages = {665--670},
title = {Can Multi-DoF Training Improve Robustness of Muscle Synergy Inspired Myocontrollers?},
url = {http://dx.doi.org/10.1109/ICORR.2019.8779520},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Non-negative Matrix Factorization (NMF) has been effective in extracting commands from surface electromyography (EMG) for the control of upper-limb prostheses. This approach enables Simultaneous and Proportional Control (SPC) over multiple degrees-of-freedom (DoFs) in a minimally supervised way. Here, like with other myoelectric approaches, robustness remains essential for clinical adoption, with device donning/doffing being a known cause for performance degradation. Previous research has demonstrated that NMF-based myocontrollers, trained on just single-DoF activations, permit a certain degree of user adaptation to a range of disturbances. In this study, we compare this traditional NMF controller with its sparsity constrained variation that allows initialization using both single and combined-DoF activations (NMF-C). The evaluation was done on 12 able bodied participants through a set of online target-reaching tests. Subjects were fitted with an 8-channel bipolar EMG setup, which was shifted by 1cm in both transversal directions throughout the experiments without system retraining. In the baseline condition NMF performed somewhat better than NMFC, but it did suffer more following the electrode repositioning, making the two perform on par. With no significant difference present across the conditions, results suggest that there is no immediate advantage from the naïve inclusion of more comprehensive training sets to the classic synergy-inspired implementation of SPC.
AU  - Yeung,D
AU  - Farina,D
AU  - Vujaklija,I
DO  - 10.1109/ICORR.2019.8779520
EP  - 670
PY  - 2019///
SP  - 665
TI  - Can Multi-DoF Training Improve Robustness of Muscle Synergy Inspired Myocontrollers?
UR  - http://dx.doi.org/10.1109/ICORR.2019.8779520
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31374707
ER  -
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