Fuzzy Logic with Kalman Filter Model Framework for Children’s Personal Health Apps

The increasing prevalence of obesity among children under five has led to a growing demand for improved food nutrition advisory systems. Current food nutrition recommendation models struggle with parameter estimation, contextual adaptation, and real-time accuracy, often relying on traditional fuzzy logic models that lack responsiveness to evolving dietary needs. This study proposes an Adaptive Extended Kalman Filter Fuzzy Logic (AEKFFL) model to enhance the accuracy and reliability of food nutrition recommendations. The AEKFFL model integrates the Extended Kalman Filter (EKF) for dynamic estimation of nutritional values and Fuzzy Logic for adaptive decision-making, effectively addressing parametric uncertainties in nutrition estimation. The research employs a Design Science Research Methodology (DSRM), incorporating stakeholder interviews, literature review, and data from food composition databases, user reviews, and ingredient information. The proposed hybrid model is tested against baseline methods, including standalone Fuzzy Logic, Support Vector Machine (SVM), Neural Networks (NN), and a hybrid Fuzzy-NN approach. Experimental results demonstrate that the AEKFFL model achieves the highest accuracy (94.8%) with the lowest error rates (MAE = 0.031, RMSE = 0.045), outperforming alternative models. Additionally, AEKFFL exhibits superior classification performance (F1-score = 94.4%) and usability (SUS score = 92.1%), indicating its effectiveness in real-time nutritional guidance. These findings suggest that AEKFFL provides an innovative and computationally efficient framework for personal health and food recommendations, contributing to enhanced dietary management and obesity prevention among children. Future work will focus on refining model adaptability and integrating real-time IoT data for further improvements in precision and responsiveness.