Purushothaman Geethanjali, Vikas Raunak
School of Electrical Engineering, VIT, Vellore, TN, 632 014, India.
Australas Phys Eng Sci Med. 2018 Jun;41(2):549-559. doi: 10.1007/s13246-018-0646-7. Epub 2018 May 9.
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.
本文重点在于识别一种有效的模式识别方案，该方案使用最少数量的时域特征来实现对假手的灵巧控制，以从表面肌电图（EMG）信号中识别各种手指运动。本研究考虑了来自8名健全受试者的8通道EMG信号，涉及15种个体和组合手指活动。在这项工作中，尝试用最少数量的特征识别多个类别。因此，使用双树复数小波变换对EMG信号进行预处理，以提高特征的辨别能力，并从预处理数据中提取诸如过零、斜率符号变化、平均绝对值和波形长度等时域特征。使用不同的分类器（如线性判别分析、朴素贝叶斯分类器、二次支持向量机和三次支持向量机），在有无特征选择算法的情况下，研究提取特征的性能。使用粒子群优化（PSO）和蚁群优化（ACO），针对不同数量的特征研究特征选择，以确定特征的影响。结果表明，采用蚁群优化的朴素贝叶斯分类器在识别15种不同手指运动时，使用16个特征，平均分类准确率为88.89%，响应时间为0.058025毫秒，与具有特征选择的支持向量机分类器相比，在显著性水平为0.05时，准确率有显著差异。有无特征选择的支持向量机分类器在准确率、特异性和敏感性方面没有显著差异。但处理时间明显长于线性判别分析和朴素贝叶斯分类器。粒子群优化和蚁群优化的结果表明，斜率符号变化有助于识别活动。在粒子群优化中，发现平均绝对值比波形长度更有效，这与蚁群优化的结果相反。此外，在这两种方法中，过零在手指运动分类中都被发现无效。
