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A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands
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Schmalfuss_JNE_Revised_final_wm.pdf | Accepted version | 834.5 kB | Adobe PDF | View/Open |
Title: | A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands |
Authors: | Schmalfuss, L Hahne, J Farina, D Hewitt, M Kogut, A Doneit, W Reischl, M Rupp, R Liebetanz, D |
Item Type: | Journal Article |
Abstract: | Objective. The conventional myoelectric control scheme of hand prostheses provides a high level of robustness during continuous use. Typically, the electrical activity of an agonist/antagonist muscle pair in the forearm is detected and used to control either opening/closing or rotation of the prosthetic hand. The translation of more sophisticated control approaches (e.g. regression-based classifiers) to clinical practice is limited mainly because of their lack of robustness in real-world conditions (e.g. due to different arm positions). We therefore explore a new hybrid approach, in which a second degree of freedom (DOF) controlled by the myoelectric activity of the posterior auricular muscles is added to the conventional forearm control. With this, an independent, simultaneous and proportional control of rotation and opening/closing of the hand is possible. Approach. In this study, we compared the hybrid auricular control system (hACS) to the two most commonly used control techniques for two DOF. Ten able-bodied subjects and one person with transradial amputation performed two standardizes tests in three different arm positions. Main results. Subjects controlled a hand prosthesis significantly more rapidly and more accurately using the hACS. Moreover, the robustness of the system was not influenced by different arm positions. Significance. The hACS therefore offers an alternative solution for simultaneous and proportional myoelectric control of two degrees of freedom that avoids several robustness issues related to machine learning based approaches. |
Issue Date: | 1-Oct-2018 |
Date of Acceptance: | 31-Jul-2018 |
URI: | http://hdl.handle.net/10044/1/67197 |
DOI: | https://dx.doi.org/10.1088/1741-2552/aad727 |
ISSN: | 1741-2552 |
Publisher: | IOP Publishing |
Journal / Book Title: | Journal of Neural Engineering |
Volume: | 15 |
Issue: | 5 |
Copyright Statement: | © 2018 IOP Publishing Ltd. This is an author-created, un-copyedited version of an article accepted for publication in Journal of Neural Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at https://dx.doi.org/10.1088/1741-2552/aad727. |
Keywords: | Science & Technology Technology Life Sciences & Biomedicine Engineering, Biomedical Neurosciences Engineering Neurosciences & Neurology prosthesis control human-machine-interfaces auricular control system UPPER-LIMB PROSTHESES OF-THE-ART PATTERN-RECOGNITION REAL-TIME SURFACE EMG CLASSIFICATION POSITION STATE ADAPTATION EXTRACTION 0903 Biomedical Engineering 1103 Clinical Sciences 1109 Neurosciences Biomedical Engineering |
Publication Status: | Published |
Article Number: | 056028 |
Online Publication Date: | 2018-08-23 |
Appears in Collections: | Bioengineering Faculty of Engineering |