TCNN-KAN: Optimized CNN by Kolmogorov-Arnold Network and Pruning Techniques for sEMG Gesture Recognition

Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscle activity. sEMG signals are widely used in the field of biomedical and health informatics for diagnosing and monitoring neuromuscular disorders, as well as in fields such as motor control, rehabilitation, and human-computer interaction. In this paper, we propose a novel model called the Triple Convolutional Neural Network and Kolmogorov-Arnold Network (TCNN-KAN) for recognizing gesture signals based on sEMG. Our approach replaces the commonly used fully connected layer with the KAN, parameterizing it as a spline function to improve classification accuracy. Specifically, when using a KAN instead, generate the TCNN-KAN-1 model. When using two KAN layers, generate the TCNN-KAN-2 model and generate the TCNN-KAN-3 model when KAN replaces all fully connected layers. Firstly, to ensure the model learns universal features, we fuse gesture signals from different individuals and segment them to create uniform window sizes. Then, the processed signal is input into the basic convolution layer of different depths for training. In order to improve the accuracy, we convert the standard fully connected layer in the convolutional layer to the KAN layer so that it has a learnable activation function in weight. Finally, we introduce unstructured pruning to reduce computational complexity and minimize overfitting by removing channels with lower feature importance. We use three datasets, NinaPro DB1, NinaPro DB5, and CSL, for evaluation. The results show that on the TCNN-KAN-2 model, each dataset has achieved the highest accuracy. Specifically, when the pruning rates were 0.2, 0.1, and 0.4, the accuracy rates reached 98.38%, 93.81%, and 75.56%, respectively.