KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition

Kolmogorov-Arnold Networks (KANs) excel at learning complex functions on clean data but struggle with noisy, real-world datasets, unlike conventional MLPs which are noise-tolerant and efficient. This paper proposes a hybrid KAN-MLP architecture for IMU-based Human Activity Recognition, strategically combining KANs for input embedding, MLPs for intermediate feature mixing, and a specialized LarctanKAN for final classification.