Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico.
Facultad de Ciencias Químicas e Ingeniería (FCQeI), Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Morelos, Mexico.
Sensors (Basel). 2022 Jun 30;22(13):4972. doi: 10.3390/s22134972.
The classification of surface myoelectric signals (sEMG) remains a great challenge when focused on its implementation in an electromechanical hand prosthesis, due to its nonlinear and stochastic nature, as well as the great difference between models applied offline and online. In this work, the selection of the set of the features that allowed us to obtain the best results for the classification of this type of signals is presented. In order to compare the results obtained, the Nina PRO DB2 and DB3 databases were used, which contain information on 50 different movements of 40 healthy subjects and 11 amputated subjects, respectively. The sEMG of each subject was acquired through 12 channels in a bipolar configuration. To carry out the classification, a convolutional neural network (CNN) was used and a comparison of four sets of features extracted in the time domain was made, three of which have shown good performance in previous works and one more that was used for the first time to train this type of network. Set one is composed of six features in the time domain (TD1), Set two has 10 features also in the time domain (TD2) including the autoregression model (AR), the third set has two features in the time domain derived from spectral moments (TD-PSD1), and finally, a set of five features also has information on the power spectrum of the signal obtained in the time domain (TD-PSD2). The selected features in each set were organized in four different ways for the formation of the training images. The results obtained show that the set of features TD-PSD2 obtained the best performance for all cases. With the set of features and the formation of images proposed, an increase in the accuracies of the models of 8.16% and 8.56% was obtained for the DB2 and DB3 databases, respectively, compared to the current state of the art that has used these databases.
表面肌电信号（sEMG）的分类在机电手假肢的应用中仍然是一个巨大的挑战，因为它具有非线性和随机性，以及离线和在线应用模型之间的巨大差异。在这项工作中，我们提出了选择特征集的方法，这些特征集允许我们获得这种类型信号分类的最佳结果。为了比较所得到的结果，使用了 Nina PRO DB2 和 DB3 数据库，其中分别包含了 40 位健康受试者和 11 位截肢受试者的 50 种不同运动的信息。每位受试者的 sEMG 通过双极配置的 12 个通道进行采集。为了进行分类，使用了卷积神经网络（CNN），并对在时域中提取的四组特征进行了比较，其中三组在以前的工作中表现良好，另一组是首次用于训练这种类型的网络。第一组由六个时域特征（TD1）组成，第二组由十个时域特征（TD2）组成，包括自回归模型（AR），第三组由两个源于谱矩的时域特征（TD-PSD1）组成，最后，一组五个时域特征也包含了信号功率谱的信息（TD-PSD2）。在每个集合中选择的特征以四种不同的方式组织，以形成训练图像。结果表明，对于所有情况，特征集 TD-PSD2 获得了最佳性能。与目前使用这些数据库的最新技术相比，使用所提出的特征集和图像形成方法，分别为 DB2 和 DB3 数据库的模型准确性提高了 8.16%和 8.56%。
