基于肘关节表面肌电信号的负载识别

针对大多数肌电信号只进行特定肢体动作识别而没有对肢体进行外加负载识别的问题,提出一种基于表面肌电信号(surface electromyography, s EMG)的负载识别方法。首先,采用4通道表面电极采集肘关节在不同负载下的s EMG信号;然后,利用时域、频域特征提取方法对s EMG信号进行特征提取构成特征向量;最后,利用支持向量机(support vector maching, SVM)、BP神经网络和RBF神经网络对特征向量进行分类识别。结果表明以时域特征值识别,SVM的识别效果最佳,准确率为96.2%;以频域特征值识别,BP神经网络的识别效果最佳,准确率为87.5%;以时、频域组合特征值识别,RBF神经网络的识别效果最佳,准确率为90.4%。可见通过s EMG信号进行负载识别具有一定的可行性,为s EMG信号的广泛应用奠定基础。

Load identification based on surface EMG signal of elbow joint

In order to solve the problem that most surface electromyography (s EMG) signals only recognize specific limb movements without additional load recognition, a load recognition method based on s EMG is proposed. First, 4-channel surface electrodes were used to collect s EMG signals of the elbow joint under different loads. Then, feature extraction method in time domain and frequency domain was used to extract features of s EMG signals to form eigenvectors. Finally, support vector machine (SVM), BP neural network and RBF neural network were used to classify and recognize eigenvectors. The results show that SVM has the best recognition effect with 96.2% accuracy in time domain, BP neural network has the best recognition effect with 87.5% accuracy in frequency domain, and RBF neural network has the best recognition effect with 90.4% accuracy in time and frequency domain. So it is feasible to identify the load through s EMG signal, which lays a foundation for the wide application of s EMG signal.