KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
Researchers have developed a hybrid neural network architecture, KAN-MLP-Mixer, that combines the precision of Kolmogorov-Arnold Networks (KANs) with the noise robustness and efficiency of Multi-Layer Perceptrons (MLPs). This approach strategically integrates KAN modules for input embedding and classification, while utilizing MLPs for intermediate feature mixing. Tested across eight public datasets, the KAN-MLP model demonstrated a 5.33% average improvement in macro F1 score over pure-MLP models, significantly outperforming standalone KAN and MLP baselines. AI
IMPACT This hybrid architecture offers improved accuracy and robustness for human activity recognition tasks using wearable sensors.