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Figure 5. The change of train accuracy and test accuracy for 50 iterations.

Figure 6. Recognition accuracy of CNN with sliding windows vs. traditional machine learning methods. CNN: Convolutional neural network;
SVM: shapley-value-based muscle.

accuracy of CNN with sliding windows and traditional machine learning methods.

In comparison to previous machine learning methods, such as k-nn, SVM, Random Forests, and LDA on the
Ninapro DB1 dataset, the CNN model achieved higher recognition accuracy in the gesture recognition task.

3.3.2 Grad-CAM results
We let the machine randomize some color images of sEMG signals and apply the Grad-CAM algorithm to
calculate the predictions of the network for the regions of interest of the input. The gradient information of the
last convolutional layer of the CNN model is first computed, and then the gradient information is weighted
and averaged with the output of the convolutional layer to obtain a feature map. Then, the feature map is
upsampled to obtain a heat map of the same size as the input image.