Institute of Intelligent Control and Robotics, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Math Biosci Eng. 2021 Apr 23;18(4):3521-3542. doi: 10.3934/mbe.2021177.
This study proposed a gait recognition method based on the deep neural network of surface electromyography (sEMG) signals to improve the stability and accuracy of gait recognition using sEMG signals of the lower limbs. First, we determined the parameters of time domain features, including the mean of absolute value, root mean square, waveform length, the number of zero-crossing points of the sEMG signals after noise elimination, and the frequency domain features, including mean power frequency and median frequency. Second, the time domain feature and frequency domain feature were combined into a multi-feature combination. Then, the classifier was trained and used for gait recognition. Finally, in terms of the recognition rate, the classifier was compared with the support vector machine (SVM) and extreme learning machine (ELM). The results showed the method of deep neural network (DNN) had a better recognition rate than that of SVM and ELM. The experimental results of the participants indicated that the average recognition rate obtained with the method of DNN exceeded 95%. On the other hand, from the statistical results of standard deviation, the difference between subjects ranged from 0.46 to 0.94%, which also proved the robustness and stability of the proposed method.
本研究提出了一种基于表面肌电信号（sEMG）的深度神经网络的步态识别方法，以提高下肢 sEMG 信号步态识别的稳定性和准确性。首先，我们确定了时域特征的参数，包括均值绝对值、均方根、波形长度、消除噪声后的 sEMG 信号的过零点数量以及频域特征，包括平均功率频率和中位数频率。其次，将时域特征和频域特征组合成多特征组合。然后，对分类器进行训练并用于步态识别。最后，从识别率的角度，将分类器与支持向量机（SVM）和极限学习机（ELM）进行比较。结果表明，深度神经网络（DNN）方法的识别率优于 SVM 和 ELM。参与者的实验结果表明，DNN 方法的平均识别率超过 95%。另一方面，从标准差的统计结果来看，受试者之间的差异在 0.46%至 0.94%之间，这也证明了所提出方法的稳健性和稳定性。
