Electromyography (EMG) pattern recognition is one of the widely used methods to control the rehabilitation robots and prostheses. However, the changes in the distribution of EMG data due to electrodes shifting results in classification decline, which hinders its clinical application in repeated uses. Adaptive learning can solve this problem but takes additional time. To address this, an efficient scheme is developed by comparing the performance of twelve combinations of three feature selection methods (no feature selection (NFS), sequential forward search (SFS), and particle swarm optimization (PSO)) and four classification methods (non-adaptive support vector machine (N-SVM), incremental SVM (I-SVM), SVM based on TrAdaBoost (T-SVM), and I-SVM based on TrAdaBoost (TI-SVM)) in the classification of EMG data of 12 subjects for 5 consecutive days. Our results showed that TI-SVM achieved the highest classification accuracy among the classification methods (p < 0.05). The SFS method achieved the same classification accuracy as that of the scheme trained with the feature vectors selected by the NFS method (p = 0.999) while achieving a lower training time than that of TI-SVM combined with the NFS method (p = 0.043). Although the PSO method outperformed the NFS and SFS methods by achieving reduced training and response times (p < 0.05), the PSO method achieved a considerably lower classification accuracy than that of the scheme trained with the feature vectors selected by the NFS (p = 0.001) or SFS (p = 0.001) method. Furthermore, TI-SVM combined with the SFS method outperformed the CNN method with fine-tuning in classification accuracy on a small data set (p = 0.001). The results indicate that TI-SVM combined with the SFS method is suitable for improving the performance of EMG pattern recognition in repeated uses.

中文翻译：

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通过将特征选择和增量转移学习相结合，提高了重复使用的EMG模式识别模型的性能

肌电图（EMG）模式识别是控制康复机器人和假肢的广泛使用的方法之一。然而，由于电极移位而引起的EMG数据分布的变化导致分类下降，这阻碍了其在重复使用中的临床应用。自适应学习可以解决此问题，但需要更多时间。为了解决这个问题，通过比较三种特征选择方法（无特征选择（NFS），顺序前向搜索（SFS）和粒子群优化（PSO））的十二种组合的性能和四种分类方法（非支持的支持向量机（N-SVM），增量SVM（I-SVM），基于TrAdaBoost的SVM（T-SVM）和基于TrAdaBoost的I-SVM（TI-SVM））对12个EMG数据进行分类连续5天的科目。我们的结果表明，TI-SVM在分类方法中达到了最高的分类精度（p <0.05）。SFS方法的分类精度与使用NFS方法选择的特征向量训练的方案的分类精度相同（p = 0.999），同时比TI-SVM与NFS方法结合的方法（p = 0.043）的训练时间短。尽管PSO方法通过减少训练和响应时间而胜过NFS和SFS方法（p <0.05），但与使用NFS选择的特征向量训练的方案相比，PSO方法实现的分类准确度要低得多（p = 0.001 ）或SFS（p = 0.001）方法。此外，在小数据集上，TI-SVM与SFS方法相结合的方法在分类精度上具有微调效果，胜过CNN方法（p = 0.001）。