Fuzzy processing applied to improve multimodal sensor data fusion to discover frequent behavioral patterns for smart healthcare

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. • Development of an IoT-based system integrating multimodal sensor networks for enhanced data acquisition in smart healthcare environments. • Application of advanced fuzzy logic techniques and rule-based systems for the processing of sensor data, enabling intelligent decision-making in health monitoring scenarios. • Fuzzification of sensor data using linguistic variables to enhance interpretability and facilitate the management of uncertainty in complex health datasets. • Derivation of association rules for the automated identification and analysis of frequent behavioral patterns, supporting proactive and personalized healthcare interventions.