Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification
Gijsberts A, Atzori M, Mueller H, Caputo B, Castellini C (2014)
Publication Type: Journal article
Publication year: 2014
Journal
Book Volume: 22
Pages Range: 735-744
Article Number: 6728705
Journal Issue: 4
DOI: 10.1109/TNSRE.2014.2303394
Abstract
There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which contains surface electromyography data for 6 DOF force activations as well as for 40 discrete hand movements. The evaluation involves a modern kernel method and compares performance of three feature representations and three kernel functions. Both the force regression and movement classification problems can be learned successfully when using a nonlinear kernel function, while the exp-χ2 kernel outperforms the more popular radial basis function kernel in all cases. Furthermore, combining surface electromyography and accelerometry in a multimodal classifier results in significant increases in accuracy as compared to when either modality is used individually. Since window-based classification accuracy should not be considered in isolation to estimate prosthetic controllability, we also provide results in terms of classification mistakes and prediction delay. To this extent, we propose the movement error rate as an alternative to the standard window-based accuracy. This error rate is insensitive to prediction delays and it allows us therefore to quantify mistakes and delays as independent performance characteristics. This type of analysis confirms that the inclusion of accelerometry is superior, as it results in fewer mistakes while at the same time reducing prediction delay. © 2001-2011 IEEE.
Authors with CRIS profile
Involved external institutions
idiap Research Institute
Switzerland (CH)
HES-SO Valais-Wallis / University of Applied Sciences and Arts Western Switzerland Valais / Fachhochschule Westschweiz - Wallis
Switzerland (CH)
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) / German Aerospace Center
Germany (DE)
Switzerland (CH)
HES-SO Valais-Wallis / University of Applied Sciences and Arts Western Switzerland Valais / Fachhochschule Westschweiz - Wallis
Switzerland (CH)
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) / German Aerospace Center
Germany (DE)
How to cite
APA:
Gijsberts, A., Atzori, M., Mueller, H., Caputo, B., & Castellini, C. (2014). Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(4), 735-744. https://dx.doi.org/10.1109/TNSRE.2014.2303394
MLA:
Gijsberts, Arjan, et al. "Movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification." IEEE Transactions on Neural Systems and Rehabilitation Engineering 22.4 (2014): 735-744.
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