COOL: Continual Online On-Device Learning for Human Activity Recognition Enhanced by KANs

Variations in deployment parameters such as new users or sensor locations are a major concern in sensor-based human activity recognition (HAR), accounting for a major source of performance drop from in-lab experiments to in-the-wild deployment. While fine-tuning a pre-trained model using data acquired during deployment can reduce such performance loss, it often leads to catastrophic forgetting. The hardware constraints of edge devices also limit the options of continual learning techniques. To address these challenges, we introduce COOL, a continual online on-device learning method leveraging Kolmogorov-Arnold Networks (KANs). Our method exploits the inherent plasticity of KANs. COOL is evaluated through two HAR scenarios (utilizing bio-impedance and Inertial Measurement Unit (IMU) signals separately) to demonstrate its performance in addressing both the catastrophic forgetting issue and concept drift issue caused by new targets (users and sensor locations). A significant average overall performance improvement of around 6.82