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首页> 外文期刊>IEEE sensors journal >Surface EMG Based Hand Manipulation Identification Via Nonlinear Feature Extraction and Classification
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Surface EMG Based Hand Manipulation Identification Via Nonlinear Feature Extraction and Classification

机译：基于表面肌电信号的非线性特征提取与分类的手部识别

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摘要

This paper proposes and evaluates methods of nonlinear feature extraction and nonlinear classification to identify different hand manipulations based on surface electromyography (sEMG) signals. The nonlinear measures are achieved based on the recurrence plot to represent dynamical characteristics of sEMG during hand movements. Fuzzy Gaussian Mixture Models (FGMMs) are proposed and employed as a nonlinear classifier to recognise different hand grasps and in-hand manipulations captured from different subjects. Various experiments are conducted to evaluate their performance by comparing 14 individual features, 19 multifeatures and 4 different classifiers. The experimental results demonstrate the proposed nonlinear measures provide important supplemental information and they are essential to the good performance in multifeatures. It is also shown that FGMMs outperform commonly used approaches including Linear Discriminant Analysis, Gaussian Mixture Models and Support Vector Machine in terms of the recognition rate. The best performance with the recognition rate of 96.7% is achieved by using FGMMs with the multifeature combining Willison Amplitude and Determinism.
机译：本文提出并评估了基于表面肌电图（sEMG）信号的非线性特征提取和非线性分类方法，以识别不同的手部操作。基于递归图实现了非线性测量，以表示手运动过程中sEMG的动力学特性。提出了模糊高斯混合模型（FGMM），并将其用作非线性分类器，以识别从不同主体捕获的不同手部抓握和手部操作。通过比较14个独立功能，19个多功能和4个不同的分类器，进行了各种实验以评估其性能。实验结果表明，所提出的非线性度量提供了重要的补充信息，它们对于在多特征中的良好性能至关重要。还表明，就识别率而言，FGMMs优于包括线性判别分析，高斯混合模型和支持向量机在内的常用方法。通过结合使用具有Willison幅值和确定性的多重功能的FGMM，可以达到96.7％的识别率，从而获得最佳性能。

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