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Kolmogorov-Arnold Networks for Sensor Data Processing: A Comprehensive Survey of Architectures, Applications, and
Antonio M Martínez-Heredia1,2, Andrés Ortiz1
1Department of Communications Engineering, University of Malaga, 29071 Malaga, Spain.
Sensors (Basel, Switzerland)
|May 4, 2026
Summary
Kolmogorov-Arnold Networks (KANs) offer interpretable and efficient alternatives to traditional neural networks for sensor data processing. This review highlights their potential and challenges in real-world sensor applications.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Sensor Data Processing
Background:
- Kolmogorov-Arnold Networks (KANs) are emerging as a novel neural architecture.
- KANs replace fixed activation functions with learnable mappings, enhancing interpretability and flexibility.
- They are particularly suited for sensor-driven systems requiring transparency and efficiency.
Purpose of the Study:
- To survey the application of KAN-based approaches for processing sensor data.
- To analyze KAN variants and their integration into deep learning pipelines.
- To identify current limitations and future challenges for KAN adoption in sensor systems.
Main Methods:
- A systematic literature review using PRISMA methodology from 2024-2026.
- Examination of KAN deployment across industrial, mechanical, medical, biomedical, and environmental sensing.
- Analysis of KAN integration with convolutional, recurrent, transformer, graph, and physics-informed architectures.
Main Results:
- KAN models show comparable performance to conventional models with fewer parameters and improved interpretability.
- KAN variants include spline-based, polynomial-based, monotonic, and hybrid formulations.
- Integration into various deep learning pipelines demonstrates KANs' versatility.
Conclusions:
- KANs present a promising, interpretable, and parameter-efficient alternative for sensor data processing.
- Challenges include computational overhead, noise sensitivity, and deployment on resource-constrained devices.
- Future work should address scalability, hardware integration, automated development, robustness, and standardized evaluation for KANs in sensor applications.
