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Optimization of EMG-Derived Features for Upper Limb Prosthetic Control

  • Polyarticulated active prostheses constitute a promising solution for upper limb amputees. The bottleneck for their adoption though, is the lack of intuitive control. In this context, machine learning algorithms based on pattern recognition from electromyographic (EMG) signals represent a great opportunity for naturally operating prosthetic devices, but their performance is strongly affected byPolyarticulated active prostheses constitute a promising solution for upper limb amputees. The bottleneck for their adoption though, is the lack of intuitive control. In this context, machine learning algorithms based on pattern recognition from electromyographic (EMG) signals represent a great opportunity for naturally operating prosthetic devices, but their performance is strongly affected by the selection of input features. In this study, we investigated different combinations of 13 EMG-derived features obtained from EMG signals of healthy individuals performing upper limb movements and tested their performance for movement classification using an Artificial Neural Network. We found that input data (i.e., the set of input features) can be reduced by more than 50% without any loss in accuracy, while diminishing the computing time required to train the classifier. Our results indicate that input features must be properly selected in order to optimize prosthetic control.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/8410
Bibliografische Angaben
Title (English):Optimization of EMG-Derived Features for Upper Limb Prosthetic Control
Conference:International Conference on Biomimetic and Biohybrid Systems (12. : July 10-13, 2023 : Genoa, Italy)
Author:Dario Di Domenico, Francesca PaganiniStaff MemberORCiD, Andrea Marinelli, Lorenzo De Michieli, Nicoló Boccardo, Marianna Semprini
Edition:1.
Year of Publication:2023
Place of publication:Cham
Publisher:Springer
First Page:77
Last Page:87
Parent Title (English):Biomimetic and Biohybrid Systems : 12th International Conference, Living Machines 2023, Genoa, Italy, July 10-13, 2023, Proceedings, Part I
Editor:Fabian Meder, Alexander Hunt, Laura Margheri, Anna Mura, Barbara Mazzolai
Volume:LNCS 14157
ISBN:978-3-031-38856-9 (Softcover)
ISBN:978-3-031-38857-6 (eBook)
DOI:https://doi.org/10.1007/978-3-031-38857-6_6
Language:English
Inhaltliche Informationen
Institutes:Fakultät Maschinenbau und Verfahrenstechnik (M+V)
Collections of the Offenburg University:Bibliografie
Tag:EMG features; Machine Learning; Myocontrol; Prosthetic control
Formale Angaben
Relevance for "Jahresbericht über Forschungsleistungen":Konferenzbeitrag: h5-Index < 30
Open Access: Closed 
Licence (German):License LogoUrheberrechtlich geschützt