Comparing Kolmogorov-Arnold Networks and Conventional Machine Learning Algorithms for sEMG Based Movement Classification - Johnson,Mia Britta (2025)
Myoelectric prosthetics utilize machine learning(ML) algorithms to translate surface electromyography(sEMG) signal patterns into movement, allowing patients to easily control their prosthesis. While the use of conventional ML algorithms has been effective within these prosthetics, challenges still remain in terms of classification accuracy and computation speed. It was hypothesized that Kolmogorov-Arnold Networks (KANs), a novel neural network known for learnable activation functions, could be used to address these issues. To test this hypothesis popular KAN libraries were evaluated for their ability to accurately classify sEMG signals from the NinaPro DB2 dataset. Various model parameters and architectures were tested for their ability to produce accurate and efficient movement classification results. Over the course of KAN testing the efficient-kan model was identified as the most successful KAN classifier. Efficient-kan was then tested against conventional ML algorithms such as MLP, KNN, RF and others. Results revealed that the efficient-kan model achieved classification accuracy comparable to several conventional ML algorithms. However, efficient-kan did not outperform the top performing conventional classifiers in terms of classification accuracy or computational efficiency. These project findings indicate a need for the further development of KAN algorithms before they are able to address current issues within myoelectric prosthetics and be considered a viable replacement for established ML algorithms.
Keywords: Kolmogorov-Arnold Networks; sEMG; myoelectric prosthetics