In this paper, we characterize the NINAPRO database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by jointly recording surface electromyography signals from the forearm and kinematics of the hand and wrist while subjects perform a predefined set of actions and postures. Besides describing the acquisition protocol, overall features of the datasets and the processing procedures in detail, we present benchmark classification results using a variety of feature representations and classifiers. Our comparison shows that simple feature representations such as mean absolute value and waveform length can achieve similar performance to the computationally more demanding marginal discrete wavelet transform. With respect to classification methods, the nonlinear support vector machine was found to be the only method consistently achieving high performance regardless of the type of feature representation. Furthermore, statistical analysis of these results shows that classification accuracy is negatively correlated with the subject's Body Mass Index. The analysis and the results described in this paper aim to be a strong baseline for the NINAPRO database. Thanks to the NINAPRO database (and the characterization described in this paper), the scientific community has the opportunity to converge to a common position on hand movement recognition by surface electromyography, a field capable to strongly affect hand prosthesis capabilities. © 2014 IEEE.

Characterization of a benchmark database for myoelectric movement classification / Atzori, Manfredo; Gijsberts, Arjan; Kuzborskij, Ilja; Elsig, Simone; Mittaz Hager, Anne Gabrielle; Deriaz, Olivier; Castellini, Claudio; Müller, Henning; Caputo, Barbara. - In: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING. - ISSN 1534-4320. - STAMPA. - 23:1(2015), pp. 73-83. [10.1109/TNSRE.2014.2328495]

Characterization of a benchmark database for myoelectric movement classification

CAPUTO, BARBARA
2015

Abstract

In this paper, we characterize the NINAPRO database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by jointly recording surface electromyography signals from the forearm and kinematics of the hand and wrist while subjects perform a predefined set of actions and postures. Besides describing the acquisition protocol, overall features of the datasets and the processing procedures in detail, we present benchmark classification results using a variety of feature representations and classifiers. Our comparison shows that simple feature representations such as mean absolute value and waveform length can achieve similar performance to the computationally more demanding marginal discrete wavelet transform. With respect to classification methods, the nonlinear support vector machine was found to be the only method consistently achieving high performance regardless of the type of feature representation. Furthermore, statistical analysis of these results shows that classification accuracy is negatively correlated with the subject's Body Mass Index. The analysis and the results described in this paper aim to be a strong baseline for the NINAPRO database. Thanks to the NINAPRO database (and the characterization described in this paper), the scientific community has the opportunity to converge to a common position on hand movement recognition by surface electromyography, a field capable to strongly affect hand prosthesis capabilities. © 2014 IEEE.
2015
Electromyography; machine learning; prosthetics; publicly available databases; Neuroscience (all); Computer Science Applications1707 Computer Vision and Pattern Recognition; Biomedical Engineering
01 Pubblicazione su rivista::01a Articolo in rivista
Characterization of a benchmark database for myoelectric movement classification / Atzori, Manfredo; Gijsberts, Arjan; Kuzborskij, Ilja; Elsig, Simone; Mittaz Hager, Anne Gabrielle; Deriaz, Olivier; Castellini, Claudio; Müller, Henning; Caputo, Barbara. - In: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING. - ISSN 1534-4320. - STAMPA. - 23:1(2015), pp. 73-83. [10.1109/TNSRE.2014.2328495]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/795343
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