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摘要: 传统基于肌电（Electromyography，EMG）的运动识别方法多是利用训练后的固定参数模型，分类已预先定义的有限个目标动作，但对肌肉疲劳导致的肌电变化，以及未定义的外部动作等干扰因素无能为力.针对这一问题，提出一种自更新混合分类模型（Self-update hybrid classification model，SUHC），该模型融合了用于排除外部动作干扰的一类支持向量机（Support vector machine，SVM），以及用于分类目标动作数据的多类线性判别分析（Linear discriminant analysis，LDA），并引入自更新机制以对抗肌电时变性干扰.通过手部动作识别实验验证提出方法的效果，在肌电大幅变化干扰下，SUHC的目标动作识别精度达到89%，对比传统的支持向量机、多层感知器（Multiple layer perceptron，MLP）和核线性判别分析（Kernel LDA，KLDA），提高了约18%，并且SUHC具备排除外部动作干扰能力，排除精度高达93%.Abstract: The traditional electromyography (EMG)-based motion recognition methods always classify a limited number of pre-defined target motions by using a trained parameter-fixed model. However, those methods have no ability to handle the interference factors, including changes of EMG signals caused by muscle fatigue and outlier motions undefined beforehand. For this problem, a self-update hybrid classification model (SUHC) is proposed. In the SUHC, the one-class support vector machine (SVM) that can reject outlier motions is combined to a multi-class linear discriminant analysis (LDA) that is used to recognize target motions; furthermore, a self-update mechanism is adopted to reduce the influence caused by EMG changes. The performance of the proposed method is verified by the experiment of EMG-based hand motion recognition. Under the interference that EMG signals vary greatly, the recognition accuracy of SUHC on target motions is about 89%, which is 18% higher than those of the normal SVM, multiple layer perceptron (MLP) and kernel LDA (KLDA); moreover, the SUHC has the ability to reject outlier motions with a 93% of rejection accuracy.1) 本文责任编委 李鸿一
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图 2 肌肉收缩及静息状态下, 采集的连续sEMG
Fig. 2 The continuous sEMG signals sampled at the states of muscle contraction and resting
图 4 识别的5种手部动作以及手部休息姿势snz
Fig. 4 The five kinds of hand motions and the snooze state (snz) that were recognized in the paper
图 5 选取的肌肉及布置的采集电极，其中ch-$i$, $i = 1, 2, 3, 4$, 表示第$i$通道电极
Fig. 5 The selected muscles and the placement of electrodes, where ch-$i$ ($i = 1, 2, 3, 4$) represents the $i$th channel electrode
图 8 由10组测试样本数据计算的MAV占其标准值比
Fig. 8 The ratio of MAV with respect to the standard value calculated by using the ten sessions of test data
图 9 将Subject-1的第1组训练数据输入SUHC (仅给出该组数据的前2个循环), 实现模型在线更新
Fig. 9 Online updating the SUHC by inputting the first session of training data of Subject-1 into the model (only plot the first two cycles of the data)
图 10 无外部动作干扰情况下, 使用Subject-1的10组测试数据计算的分类精度
Fig. 10 In the case of no outlier-motion interference, the classification accuracies calculated by using the ten sessions of test data of Subject-1
图 11 使用Subject-1的1组测试数据获得的动作识别结果
Fig. 11 The motion recognition results obtained by using one session of test data of Subject-1
图 12 存在外部动作(rng)干扰情况下, 使用Subject-1的10组测试数据获得的识别结果
Fig. 12 In the case of outlier-motion (rng) interference, the recognition results obtained by using ten sessions of test data of Subject-1
图 13 使用Subject-1的测试数据, 计算的分类精度与排除精度的均值和标准差
Fig. 13 The means and standard deviations of classification and rejection accuracies computed by using the test data of Subject-1
表 1 使用所有测试者的10组测试样本计算的平均分类精度与标准差(%)
Table 1 The mean classification accuracies and standard deviations calculated by using the ten sessions of test data of all subjects (%)
SUHC SVM MLP KLDA 1 ${\bf{91.5\pm4.3}}$ $89.3\pm5.1$ $90.4\pm2.9$ $89.8\pm5.9$ 2 ${\bf{89.1\pm2.6}}$ $85.1\pm7.5$ $81.7\pm6.4$ $83.4\pm8.1$ 3 ${\bf{88.7\pm6.6}}$ $75.3\pm10.4$ $74.3\pm8.8$ $77.1\pm10.7$ 4 ${\bf{86.2\pm4.1}}$ $70.8\pm14.4$ $71.2\pm10.7$ $72.7\pm11.2$ 5 ${\bf{93.4\pm3.8}}$ $73.0\pm9.1$ $70.7\pm13.5$ $71.5\pm14.6$ 6 ${\bf{88.5\pm6.2}}$ $69.8\pm12.9$ $64.2\pm11.2$ $67.3\pm11.9$ 7 ${\bf{86.0\pm5.7}}$ $59.9\pm12.3$ $59.5\pm12.1$ $62.1\pm13.4$ 8 ${\bf{88.1\pm8.4}}$ $61.7\pm10.2$ $57.8\pm13.0$ $59.4\pm14.7$ 9 ${\bf{91.4\pm4.9}}$ $58.4\pm9.7$ $60.1\pm10.6$ $61.6\pm9.2$ 10 ${\bf{87.9\pm4.3}}$ $62.8\pm10.3$ $57.0\pm15.6$ $58.1\pm13.9$ $m\pm st$ ${\bf{89.1\pm5.1}}$ $70.6\pm10.2$ $68.7\pm10.5$ $70.3\pm11.4$ 
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表 2 使用所有测试者的测试数据计算的分类精度与排除精度的均值和标准差(%)
Table 2 The means and standard deviations of classification and rejection accuracies computed by using the test data of all subjects (%)
目标动作分类精度 外部动作排除精度 SUHC $90.4\pm4.4$ $93.5\pm3.0$ SVDD $82.1\pm8.7$ $91.9\pm5.2$ SVM $62.5\pm16.1$ $-$ MLP $60.8\pm18.4$ $-$ KLDA $60.1\pm15.7$ $-$
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