Gesture recognition based on Gramian angular difference field and multi-stream fusion methods

Surface electromyography-based gesture recognition was widely applied in human-computer interaction, hand rehabilitation, prosthetic control, and other fields. Electromyography (EMG) signals-based gesture classification usually relies on handcrafted feature extraction with intense subjectivity or convolutional neural networks with redundant structures to extract features. This paper converts the raw EMG signals into Gramian Angular Difference Field (GADF) and Gramian Angular Summation Field images. Four models were used to classify the pictures: K-nearest Neighbors (KNN), Generalized Learning Systems, Binary Trees, and Convolutional Neural Networks using MobileNetv1, and the proposed method was verified by using the public dataset NinaproDB2. Experimental results: When the window size is 300 ms, the step size is 10 ms, and KNN are used as the classification model, the average accuracy of EMG signals classification based on the GADF method is 98.17%, and the accuracy of exercises B, C and D was 96.65%, 95.53%, and 98.02%, respectively. The recognition accuracy was 7.92%, 14.25%, and 4.279% higher than the provided baseline.