Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization

Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By a...

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Veröffentlicht in:Journal of neural engineering Jg. 15; H. 2; S. 026017 - 26025
Hauptverfasser: Lin, Chuang, Wang, Binghui, Jiang, Ning, Farina, Dario
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England IOP Publishing 01.04.2018
Schlagworte:
muscle synergy
myoelectric signal processing
prosthetic control
sparseness constraint non-negative matrix factorization
ISSN:1741-2560, 1741-2552, 1741-2552
Online-Zugang:Volltext
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Zusammenfassung:Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.
Bibliographie:JNE-101816.R1
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SourceType-Scholarly Journals-1
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ISSN:1741-2560
1741-2552
1741-2552
DOI:10.1088/1741-2552/aa9666