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...
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| Language: | en |
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The Science And Information (SAI) Organization Limited
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29530/2/0094403112025.pdf http://eprints.utem.edu.my/id/eprint/29530/ https://thesai.org/Downloads/Volume16No3/Paper_69-Fuzzy_Logic_with_Kalman_Filte_Model_Framework.pdf |
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| author | Yusop, Noorrezam Kamalrudin, Massila Mustafa, Nuridawati Moketar, Nor Aiza |
| author_facet | Yusop, Noorrezam Kamalrudin, Massila Mustafa, Nuridawati Moketar, Nor Aiza |
| author_sort | Yusop, Noorrezam |
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| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.utem.eprints-29530 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | The Science And Information (SAI) Organization Limited |
| record_format | eprints |
| spelling | my.utem.eprints-295302026-02-23T03:46:46Z http://eprints.utem.edu.my/id/eprint/29530/ Fuzzy logic with Kalman filter model framework for children’s personal health apps Yusop, Noorrezam Kamalrudin, Massila Mustafa, Nuridawati Moketar, Nor Aiza 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. The Science And Information (SAI) Organization Limited 2025 Article PeerReviewed text en cc_by_4 http://eprints.utem.edu.my/id/eprint/29530/2/0094403112025.pdf Yusop, Noorrezam and Kamalrudin, Massila and Mustafa, Nuridawati and Moketar, Nor Aiza (2025) Fuzzy logic with Kalman filter model framework for children’s personal health apps. International Journal Of Advanced Computer Science And Applications (IJACSA), 16 (3). pp. 699-706. ISSN 2158-107X https://thesai.org/Downloads/Volume16No3/Paper_69-Fuzzy_Logic_with_Kalman_Filte_Model_Framework.pdf 10.14569/IJACSA.2025.0160369 |
| spellingShingle | Yusop, Noorrezam Kamalrudin, Massila Mustafa, Nuridawati Moketar, Nor Aiza Fuzzy logic with Kalman filter model framework for children’s personal health apps |
| title | Fuzzy logic with Kalman filter model framework for children’s personal health apps |
| title_full | Fuzzy logic with Kalman filter model framework for children’s personal health apps |
| title_fullStr | Fuzzy logic with Kalman filter model framework for children’s personal health apps |
| title_full_unstemmed | Fuzzy logic with Kalman filter model framework for children’s personal health apps |
| title_short | Fuzzy logic with Kalman filter model framework for children’s personal health apps |
| title_sort | fuzzy logic with kalman filter model framework for children’s personal health apps |
| url | http://eprints.utem.edu.my/id/eprint/29530/2/0094403112025.pdf http://eprints.utem.edu.my/id/eprint/29530/ https://thesai.org/Downloads/Volume16No3/Paper_69-Fuzzy_Logic_with_Kalman_Filte_Model_Framework.pdf |
| url_provider | http://eprints.utem.edu.my/ |