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Main Title: Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing
Author(s): Hwang, Han-Jeong
Hahne, Janne Mathias
Müller, Klaus-Robert
Type: Article
Language Code: en
Abstract: There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically reliable myoelectric control. To this end, a real-time target acquisition task was performed by fourteen subjects including one individual with congenital upper-limb deficiency, where the impacts of arm position change, donning/doffing and a combination of both factors on control performance was systematically evaluated. The changes in online performance were examined with seven different performance metrics to comprehensively evaluate various aspects of myoelectric controllability. As a result, arm position change significantly affects offline prediction accuracy, but not online control performance due to real-time feedback, thereby showing no significant correlation between offline and online performance. Donning/doffing was still problematic in online control conditions. It was further observed that no benefit was attained when using a control model trained with multiple position data in terms of arm position change, and the degree of electrode shift caused by donning/doffing was not severely associated with the degree of performance loss under practical conditions (around 1 cm electrode shift). Since this study is the first to concurrently investigate the impacts of arm position change and donning/doffing in practical myoelectric control situations, all findings of this study provide new insights into robust myoelectric control with respect to arm position change and donning/doffing.
Issue Date: 2017
Date Available: 17-Jan-2018
DDC Class: 006 Spezielle Computerverfahren
Subject(s): prosthetics
linear regression analysis
control systems
electrode potentials
Sponsor/Funder: DFG, TH 662/19-1, Open Access Publizieren 2017 - 2018 / Technische Universität Berlin
Journal Title: PLoS one
Publisher: PLoS
Publisher Place: Lawrence, Kan.
Volume: 12
Issue: 11
Article Number: e0186318
Publisher DOI: 10.1371/journal.pone.0186318
ISSN: 1932-6203
Appears in Collections:FG Maschinelles Lernen » Publications

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