KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
This paper proposes a hybrid architecture combining Kolmogorov-Arnold Networks (KANs) and multi-layer perceptrons (MLPs) for IMU-based Human Activity Recognition (HAR). The KAN-MLP mixer uses a KAN input embedding, MLP intermediate mixing, and a LarctanKAN classifier. On eight public datasets, it achieves 5.33% average macro F1 improvement over pure MLP, outperforming both standalone KAN and MLP baselines, and boosts other state-of-the-art HAR architectures.
[2605.19031] KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
[Submitted on 18 May 2026]
Title:KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition
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Abstract:Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to maintain performance on noisy and imperfect real-world datasets. In contrast, conventional multi-layer perceptrons (MLPs) are far more tolerant to noise and computationally efficient. Replacing all MLP components with KANs in HAR models often degrades accuracy and computation efficiency, highlighting an open challenge: how to combine KANs' precision with MLPs' noise robustness and efficiency. To address this, we systematically explore various placements of KAN modules within deep HAR networks and propose a hybrid architecture that strategically synergizes the strengths of both paradigms, which uses a KAN-based input embedding layer, retains MLP layers for intermediate feature mixing, and introduces a specialized LarctanKAN module for final activity classification. Across eight public HAR datasets, the hybrid KAN-MLP model achieves an average macro F1 score relative improvement of 5.33\% compared pure-MLP model, significantly outperforming standalone KAN and MLP baselines. Furthermore, integrating this hybrid strategy into other state-of-the-art HAR architectures consistently boosts their performance. Our findings demonstrate that a carefully orchestrated combination of KAN, MLP, or other conventional neural components yields more robust and accurate HAR models for real-world wearable sensing environments.
Comments: 24 pages, and 9 figures
Subjects:
Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
Cite as: arXiv:2605.19031 [cs.AI]
(or arXiv:2605.19031v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.19031
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mengxi Liu [view email] [v1] Mon, 18 May 2026 18:55:46 UTC (1,862 KB)
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