A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands
File(s)Schmalfuss_JNE_Revised_final_wm.pdf (834.5 KB)
Accepted version
Author(s)
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.
Date Issued
2018-10-01
Date Acceptance
2018-07-31
Citation
Journal of Neural Engineering, 2018, 15 (5)
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.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000442650800006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
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
Publication Status
Published
Article Number
056028
Date Publish Online
2018-08-23