Kolmogorov–Arnold Networks for Cross-Domain Time-Series Modeling in Health and Activity Monitoring

Hamza Haruna Mohammed, Gabriel Kiss, Frank Lindseth

Keywords: Kolmogorov–Arnold Networks (KAN) Interpretable Deep Learning Time Series Modeling Cross-Domain Transfer Learning Wearable Sensor Data Circadian Rhythms Digital Health Attention Mechanisms

Abstract: Time-series data from wearable sensors and clinical assessments provide complementary perspectives on human health, yet they often remain siloed across domains. This work presents a framework for harmonizing heterogeneous time-series sources at both minute and daily resolutions, extracting interpretable temporal features through techniques such as frequency-domain analysis and automated feature engineering. On top of this feature space, we benchmark conventional machine learning methods, Random Forest, Logistic Regression, Gradient Boosting, and a Transformer baseline against a proposed Kolmogorov–Arnold Networks (KANs) model, which adaptively learn functional transformations tailored to complex temporal patterns. We evaluate models on tasks including activity index prediction and disorder-related classification, with a focus on transfer learning across lifestyle and clinical domains. Results indicate that KANs achieve competitive performance and offer greater interpretability of temporal dynamics than black-box architectures. The proposed framework demonstrates how modern time-series models can enable cross-domain learning and improve the understanding of physiological and behavioral health patterns.

Serve As Reviewer: ~Hamza_Gbada1, ~Fabien_Danieau1

Submission Number: 66