Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia.
IEEE Trans Biomed Eng. 2010 Jun;57(6):1410-9. doi: 10.1109/TBME.2009.2039480. Epub 2010 Feb 17.
Developing accurate and powerful electromyogram (EMG) driven prostheses controllers that can provide the amputees with effective control on their artificial limbs, has been the focus of a great deal of research in the past few years. One of the major challenges in such research is extracting an informative subset of features that can best discriminate between the different forearm movements. In this paper, a new dimensionality reduction method, referred to as orthogonal fuzzy neighborhood discriminant analysis (OFNDA), is proposed as a response to such a challenge. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed OFNDA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The proposed OFNDA is validated on EMG datasets collected from seven subjects performing a range of 5 to 10 classes of forearm movements. Practical results indicate the significance of OFNDA in comparison to many other feature projection methods (including locality preserving and uncorrelated variants of discriminant analysis) with accuracies ranging from 97.66% to 87.84% for 5 to 10 classes of movements, respectively, using only two EMG electrodes.
开发能够为截肢者提供有效控制人工肢体的精确而强大的肌电图 (EMG) 驱动假肢控制器，一直是过去几年研究的重点。在这种研究中，主要挑战之一是提取出信息量最大的特征子集，这些特征子集可以最好地区分不同的前臂运动。在本文中，提出了一种新的降维方法，称为正交模糊邻域判别分析 (OFNDA)，以应对这一挑战。与现有的模糊线性判别分析尝试不同，所提出的 OFNDA 的目标是最小化属于同一类的样本之间的距离，最大化不同类别的中心之间的距离，同时考虑到样本对不同类别的贡献。在所提出的 OFNDA 中，对从七个执行 5 到 10 个类别的前臂运动的受试者采集的 EMG 数据集进行了验证。实际结果表明，与许多其他特征投影方法（包括局部保持和判别分析的不相关变体）相比，所提出的 OFNDA 的重要性，其准确率范围分别为 97.66%至 87.84%，用于 5 到 10 类运动，仅使用两个 EMG 电极。
