DARKAN: Daily Activity Recognition Using Optimized Wavelet-Based Kolmogorov-Arnold Networks

Abstract

Human Activity Recognition (HAR) plays a crucial role in intelligent healthcare, smart environments, and elderly monitoring. Traditional deep learning-based HAR methods often function as black-box models, limiting their interpretability. The recently proposed Kolmogorov-Arnold Network (KAN) utilizes explicit, mathematically defined basis functions, which clarify its operation and enhance interpretability. However, these methods still face challenges, such as slow training speed, high computational costs and suboptimal performance. Here we propose the Daily Activity Recognition with Optimized Wavelet-based KAN (DARKAN), a lightweight architecture that leverages wavelet decomposition to boost performance, and simplifies KAN structure to lower model parameters and computational complexity. Specifically, low- and high-frequency inertial measurement unit (IMU) signals are extracted by a wavelet transform, while time-domain features are incorporated to enrich feature representation. Subsequently, the B-Spline is replaced by the wavelet function as the activation function in KAN (wav-KAN), and the network depth of wav-KAN is reduced to two layers. Finally, the optimized wav-KAN is utilized to classify daily activities by fusing the extracted time-frequency features. Extensive experiments on three open-source datasets demonstrate that DARKAN outperforms stat-eof-the-art methods, achieving 98.82%, 97.11% and 98.57% in classification accuracy respectively while reducing the number of model parameters by 1.45× and FLOPs by 4×.