The movement error rate for evaluation of machine learning methods for sEMG-based hand movement classification

Gijsberts, Arjan (the Institute de Recherche Idiap, Martigny, Switzerland) ; Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Castellini, Claudio (Robotics and Mechatronics Center of the DLR German Aerospace Center, Germany) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Caputo, Barbara (Institut de Recherche Idiap, Martigny, Switzerland)

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 non-linear 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 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.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Date:
2014
Published in:
IEEE Transactions on neural systems and rehabiliation engineering
Numeration (vol. no.):
juillet 2014, vol. 22, issue 4, pp. 735-744
DOI:
ISSN:
1534-4320
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2015-11-19, last modified 2019-02-11

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