Hwang Han-Jeong, Hahne Janne Mathias, Müller Klaus-Robert
Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gyeongbuk-do, Gumi, Republic of Korea.
Neurorehabilitaiton Systems Research Group, Department of Trauma Surgery, Orthopedic Surgery and Hand Surgery, Universiy Medical Center Goettingen, Goettingen, Germany.
PLoS One. 2017 Nov 2;12(11):e0186318. doi: 10.1371/journal.pone.0186318. eCollection 2017.
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.
存在一些实际因素,如手臂位置变化和穿脱(假肢),这些因素会妨碍稳健的肌电控制。本研究的目的是精确表征这两个代表性因素在实际控制情况下对肌电可控性的影响,从而提供有用的参考,有可能用于为临床可靠的肌电控制找到更好的解决方案。为此,包括一名先天性上肢缺失个体在内的14名受试者执行了一项实时目标获取任务,系统评估了手臂位置变化、穿脱(假肢)以及这两个因素的组合对控制性能的影响。使用七种不同的性能指标检查在线性能的变化,以全面评估肌电可控性的各个方面。结果表明,手臂位置变化显著影响离线预测准确性,但由于实时反馈对在线控制性能没有影响,因此离线和在线性能之间没有显著相关性。在在线控制条件下,穿脱(假肢)仍然存在问题。进一步观察到,在手臂位置变化方面,使用用多个位置数据训练的控制模型没有益处,并且在实际条件下(电极移位约1厘米),穿脱(假肢)引起的电极移位程度与性能损失程度没有严重关联。由于本研究首次同时研究了实际肌电控制情况下手臂位置变化和穿脱(假肢)的影响,本研究的所有发现为关于手臂位置变化和穿脱(假肢)的稳健肌电控制提供了新的见解。