Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis
Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis
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DOI：
10.1109/tnsre.2009.2039590
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发表时间：
2010-02-01
影响因子：
4.9
通讯作者：
Hudgins, Bernard S.
中科院分区：
文献类型：
--
作者：
Hargrove, Levi J.;Scheme, Erik J.;Hudgins, Bernard S.
本文介绍了一种新的模式识别为基础的肌电控制系统，使用并行二进制分类和类特定的阈值。该系统设计有一个直观的配置界面，类似于现有的传统肌电控制系统。该系统进行了评估，定量与分类错误的度量和功能与晾衣夹测试在虚拟环境中实现。对于每种情况下，所提出的系统进行了比较，一个国家的最先进的模式识别系统的基础上线性判别分析和传统的肌电控制方案与模式切换。这些评估表明，所提出的控制系统具有更高的分类误差（p < 0.001），但通过在虚拟环境中实施的晾衣夹可用性测试测量，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，适合实时嵌入式实现。这项工作提供了一个临床上可行的模式识别为基础的肌电控制系统，这是强大的，易于配置，和高度可用的基础。
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.