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.