A Conditional KAN Diffusion Network for Human Activity Recognition with Missing Sensor Signal Series

Human Activity Recognition (HAR) is crucial for applications like urban traffic management and health monitoring but faces challenges in handling complex patterns and missing sensor data. In this work, we propose a conditional Kolmogorov-Arnold network diffusion (CKAD) framework for HAR, which separates the prediction process into two stages: sensor data recovery and HAR classification. In data recovery progress, we employ the diffusion generation framework and propose a KAN-denosing model to enhance the ability for sensor feature construction. Moreover, we design the Conditional Information Interpolation mechanism to obtain the robust motion sensor representation. Experiments on three public datasets demonstrate that our model outperforms existing state-of-the-art methods, especially in scenarios with incomplete sensor data, proving its robustness and efficacy.