Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals
Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals
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DOI：
10.1007/s13246-018-0646-7
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发表时间：
2018-06-01
影响因子：
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通讯作者：
Vikas, Raunak
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文献类型：
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作者：
Purushothaman, Geethanjali;Vikas, Raunak
本文着重于识别一个有效的模式识别方案，最少的时间域功能的灵巧控制假手识别的各种手指运动的表面肌电图（EMG）信号。本工作考虑了8名健康受试者15名的八通道肌电和手指的联合活动。在这项工作中，已尝试识别一些类的最少数量的功能。为此，采用双树复小波变换对肌电信号进行预处理，提高特征的鉴别能力，并从预处理后的数据中提取过零、斜率符号变化、平均绝对值、波形长度等时域特征。采用线性判别分析、朴素贝叶斯分类器、二次支持向量机和三次支持向量机等不同分类器，研究了特征选择算法对提取特征的影响。采用粒子群优化算法（PSO）和蚁群优化算法（ACO）对特征选择问题进行了研究，并对不同特征数下的特征选择效果进行了分析。结果表明，朴素贝叶斯分类器与蚁群优化显示了88.89%的平均分类准确率与响应时间为0.058025 ms，用于识别15个不同的手指运动与16个特征具有显着差异的准确性相比，SVM分类器与特征选择的显着性水平为0.05。有和没有特征选择的SVM分类器的准确性，特异性和灵敏度没有显着差异。但处理时间明显多于LDA和NB分类器。PSO和ACO的结果表明，斜率符号的变化有助于识别活动。在PSO中，平均绝对值已被发现是有效的波形长度相比，与ACO矛盾。此外，已经发现过零点在两种方法中对手指运动的分类都是无效的。
This paper focuses on identification of an effective pattern recognition scheme with the least number of time domain features for dexterous control of prosthetic hand to recognize the various finger movements from surface electromyogram (EMG) signals. Eight channels EMG from 8 able-bodied subjects for 15 individuals and combined finger activities have been considered in this work. In this work, an attempt has been made to recognize a number of classes with the least number of features. Therefore, EMG signals are pre-processed using dual tree complex wavelet transform to improve the discriminating capability of features and time domain features such as zero crossing, slope sign change, mean absolute value, and waveform length is extracted from the pre-processed data. The performance of extracted features is studied with different classifiers such as linear discriminant analysis, naive Bayes classifier, quadratic support vector machine and cubic support vector machine with and without feature selection algorithms. The feature selection has been studied using particle swarm optimization (PSO) and ant colony optimization (ACO) with different number of features to identify the effect of features. The results demonstrated that naive Bayes classifier with ant colony optimization shows an average classification accuracy of 88.89% with a response time of 0.058025 ms for recognizing the 15 different finger movements with 16 features with significant difference in accuracy compared to SVM classifier with feature selection for a significance level of 0.05. There is no significant difference in the accuracy, specificity and sensitivity of an SVM classifier with and without feature selection. But the processing time is significantly more than the LDA and NB classifier. The PSO and ACO results revealed that slope sign changes contribute to recognizing the activity. In PSO, mean absolute value has been found to be effective compared to waveform length, contradictory with ACO. Further, the zero crossings have been found to be not effective in classification of finger movements in both the methods.