# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 2 Experimental Method
### Classification Methods  
Two steps were generally followed in this stage: (a) model training, where BPNN, SVM, and k-NN were trained for the gait phase class, and (b) testing, and then the accuracy of the gait recognition was obtained. For both training and testing steps, eight-dimensional feature vectors (four channels, with each channel including two types of features, _RMS_ and _iEMG_) from each segmented sEMG in the previous stage were used as inputs for the three training models.  
Figure 2: Schematic structure of the BPNN, including 20 nodes in the hidden layer, where eight features were extracted as input from the segmented sEMG signal after amplification, filtering, and segmenting, and the output layer consisted of a single node (phase).  
A back-propagation neural network (BPNN) is a multilayer feedforward network that is trained by error back-propagation. The basic idea is a gradient descent method, which uses the gradient search technique to achieve the minimum error mean variance of the actual output value and the expected output value of the network. Because of its remarkable performance in nonlinear mapping and flexible network structure, BPNN has been widely used for pattern recognition and classification based on sEMG signals [17; 30]. In this study, we constructed a three-layer BPNN, and set 20 nodes in the hidden layer; the classification scheme is shown in Figure 2. The input layer consisted of eight nodes (four channels of sEMG signals \(\times\) two features) from the 20 ms long analysis windows, and the output layer consisted of a single node, which was the gait phase of the corresponding analysis window.  
The support vector machine (SVM) is a machine learning method based on statistics. The key is to choose a reasonable kernel function with which the vector is mapped to a higher dimensional space, in order to determine the nonlinear relationship between an input and output. The selection of the appropriate kernel function has greater significance when the data cannot be linearly separated. [PERSON] et al. [31] demonstrated a good classification result for linearly inseparable data using a library for support vector machines (LIB-SVM) to develop the SVM classifier. In our study, the Gauss radial basis function (RBF) was employed to build the SVM model because of its good adaptability to the size of the data, and its good robustness [31; 32]. A cross-validation was applied to structure the classification model in the training phase.  
The k-NN classification algorithm is one of the simplest methods of the classification techniques. The core idea of k-NN is that if most of the k-nearest neighbor samples of a sample belong to a category in the feature space, the sample belongs to the category too, and accordingly it possesses the characteristics of this type of sample. Despite its simplicity, the k-NN method had been used successfully in a wide variety of classification problems [27; 33]. In our study, we used the Euclidean distance in the feature space to evaluate which k-training samples were the nearest neighbors [33]. Because 50 gait recycles were performed at each trial for every subject, we compared several k values (from 2 to 10), and finally chose k = 5 as the proper value for the k-NN model, for its good classification performance and efficiency.