Fuzzy processing applied to improve multimodal sensor data fusion to discover frequent behavioral patterns for smart healthcare Fernández Basso, Carlos Jesús Díaz-Jiménez, David López, Jose L. Espinilla, Macarena Data fusion Sensor data Sensor fuzzification Smart healthcare This result has been partially supported by grant PID2021- 123960OB-I00, PDC2023-145863-I00 and grant PID2021-127275OB-I00 funded by MICIU/AEI, Spain/10.13039/501100011033 and by “ERDF A way of making Europe”, grant PDC2023-145863-I00 funded by MICIU/AEI, Spain/ 10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”, and grant M.2 PDC_000756 funded by Consejería de Universidad, Investigación e Innovación, Spain and by ERDF Andalusia Program 2021–2027. Finally, the research reported in this paper is also funded by the European Union (BAG-INTEL project, grant agreement no. 101121309). Funding for open access charge: Universidad de Granada / CBUA. The extraction and utilization of latent information from sensor data is gaining increasing prominence due to its potential for transforming decision-making processes across various sectors. Data mining techniques provide robust tools for analyzing large-scale data generated by advanced network management systems, offering actionable insights that drive operational efficiency and strategic improvements. However, the sheer volume of sensor data, combined with challenges related to real-world sensor deployment and user interaction, necessitates the development of advanced data fusion and processing frameworks. This paper presents an innovative automatic fusion and fuzzification methodology designed to integrate multi-source sensor data into coherent, high-quality intelligent outputs. By applying fuzzy logic, the proposed system enhances the interpretability and interoperability of complex sensor datasets. The approach has been validated in a real-world scenario within sensorized homes of Type II diabetic patients in Cabra (Córdoba, Spain), where it aids healthcare professionals in monitoring and optimizing patient routines. Experimental results demonstrate the system’s effectiveness in identifying and analyzing behavioral patterns, highlighting its potential to improve patient care through advanced sensor data fusion techniques. 2025-06-13T10:43:45Z 2025-06-13T10:43:45Z 2025-05-20 journal article Fernandez-Basso, C., Díaz-Jimenez, D., López, J. L., & Espinilla, M. (2025). Fuzzy processing applied to improve multimodal sensor data fusion to discover frequent behavioural patterns for smart healthcare. Information Fusion, 103307. https://doi.org/10.1016/j.inffus.2025.103307 https://hdl.handle.net/10481/104644 10.1016/j.inffus.2025.103307 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elservier