Fuzzy EMG classification for prosthesis control
Fuzzy EMG classification for prosthesis control
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DOI：
10.1109/86.867872
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发表时间：
2000-09-01
期刊：
影响因子：
--
通讯作者：
Parker, PA
中科院分区：
文献类型：
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作者：
Chan, FHY;Yang, YS;Parker, PA
本文提出了一种对单点肌电图（EMG）信号进行分类的模糊方法，用于多功能假肢控制。虽然分类问题是本文的重点，但最终目标是提高肌电系统的控制性能，而分类是控制中必不可少的步骤。将时间分段的特征馈入模糊系统进行训练和分类，为了获得可接受的训练速度和真实的模糊系统结构，在训练阶段开始时使用Basic Isodata算法对这些特征进行无监督聚类，并将聚类结果用于初始化模糊系统参数，然后使用反向传播算法训练系统中的模糊规则。在四个受试者上将模糊方法与人工神经网络（ANN）方法进行比较，获得了非常相似的分类结果。它至少在三点上优于后者：识别率略高；对过度训练不敏感；一致的输出表明更高的可靠性。还讨论了模糊方法相对于 ANN 方法的一些潜在优势。
This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification, In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters, Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.