Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B5A3, Canada.
IEEE Trans Neural Syst Rehabil Eng. 2010 Feb;18(1):49-57. doi: 10.1109/TNSRE.2009.2039590. Epub 2010 Jan 12.
This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error ( p < 0.001) but yielded a more controllable myoelectric control system ( p < 0.001) as measured through a clothespin usability test implemented in a virtual environment. Furthermore, the system was computationally simple and applicable for real-time embedded implementation. This work provides the basis for a clinically viable pattern recognition based myoelectric control system which is robust, easily configured, and highly usable.
本文描述了一种新颖的基于模式识别的肌电控制系统，该系统使用并行二进制分类和特定类别的阈值。该系统采用了直观的配置界面，类似于现有的传统肌电控制系统。该系统使用分类错误度量进行定量评估，并在虚拟环境中实现了夹钳测试进行功能评估。在每种情况下，将所提出的系统与基于线性判别分析的最先进的模式识别系统和具有模式切换的传统肌电控制方案进行了比较。这些评估表明，所提出的控制系统具有更高的分类错误（p<0.001），但通过在虚拟环境中实现的夹钳可用性测试，产生了更可控的肌电控制系统（p<0.001）。此外，该系统计算简单，适用于实时嵌入式实现。这项工作为基于模式识别的肌电控制系统提供了基础，该系统具有稳健、易于配置和高度可用的特点。
