A Fusion Dimension Reduction Method for the Features of Surface Electromyographic Signals.

Surface electromyographic signals (sEMG) usually have high-dimensional properties, and direct processing of these data consumes significant computational resources. Dimensionality reduction processing can reduce the dimension of the data and improve the real-time performance and response speed. This is especially important for application scenarios such as prosthetic control and rehabilitation training where rapid feedback is required. This paper proposes a feature fusion dimension reduction method for sEMG signals. This method is constructed based on the unique correlation between the features of sEMG. To test the performance of the new dimension reduction method, the sEMG signals from five leg movements were collected from eight subjects and the classification of the feature matrix before and after dimension reduction was tested by six classifiers. The results show that the feature matrix after fusion dimension reduction has excellent classification performance in the subsequent classification tasks. It produces up to 98.3% accuracy. And the highest comprehensive evaluation index can reach 0.9958. This paper also compares the new method with three commonly used dimensionality reduction methods. The results show that the performance of the new method is not only optimal but also extremely stable. Because its classification performance will not be lower than other dimensionality reduction methods due to the change of classifiers. This confirms that the new method has a higher utility value in sEMG signals processing compared to other dimension reduction methods.