Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review
Abstract
:1. Introduction
Study Purpose, Strengths, and Limitations
2. Methods
- Publication date was outside of the time frame 2021 to 2024;
- Document type was not an article, review, or conference paper;
- Publication subject area was not computer science;
- Publication was not peer-reviewed;
- Publication language was not English;
- Duplicate entries were excluded as well.
- Publication title and keywords, abstract, or full text indicated that the publication was out of the scope and objectives of the present review, i.e., indicated that the publication was irrelevant to AI and ML technologies for PN
3. Results
3.1. Recommenders in Personalised Nutrition
- Nutrition Recommendation Systems. Generate daily or weekly meal plans tailored to individual profiles, leveraging AI, ML, or other computing technologies, as well as multidimensional data.
- Recipe Recommendation Systems. Suggest personalised recipes based on individual profiles, preferences, and other data.
- Restaurant Recommendation Systems. Recommend appropriate selections from restaurant menus to individuals based on their profiles.
3.1.1. Nutrition Recommendation Systems
3.1.2. Recipe Recommendation Systems
3.1.3. Restaurant Recommendation Systems
3.1.4. Summarization
3.2. Data Collection Technologies
3.2.1. Wearable Sensing Devices
3.2.2. Cameras
3.2.3. Smartphones and Applications
3.2.4. Other Sensing Devices
3.2.5. Summarization
4. Discussion
Research Challenges in Recommendation Systems for Personalised Nutrition
- Research challenge: Integration of diverse datasets and development of standardised protocols for multimodal data collection and analysis
- Research challenge: Friendlier and more easily accessible means (devices, methods, etc.) for data collection.
- Research challenge: Understanding the interactions between genes, diet, lifestyle, the microbiome, and more via novel sophisticated analytical methods and respective computational tools.
- Research challenge: Improvement of existing or development of novel technologies (e.g., smart devices) for gathering of additional data.
- Research challenge: Developing personalised recommendation systems that account for genetic variability.
- Research challenge: Identifying relevant genetic variants and understanding how they interact with specific nutrients or dietary patterns via large-scale studies and advanced statistical techniques.
- Research challenge: Conduct long-term studies with large sample sizes while overcoming any respective logistical challenges and financial constraints.
- Research challenge: Conduct long-term studies with large numbers of real users using diet recommendation systems, carefully monitoring their responses throughout the process (nutrition behavioural changes, real health changes/outcomes achieved, etc.).
- Research challenge: Understand how to effectively motivate and support individuals in making sustainable behaviour changes.
- Research challenge: Facilitate privacy protection and address ethical concerns related to data ownership, consent, and potential discrimination.
- Research challenge: Utilisation of technologies such as blockchain to trace the origin of food/products or geolocation systems to track their journey from farm to fork, with the aim of contributing to reductions in the carbon footprint.
- Research challenge: Development of technological solutions to facilitate precise calculations, with the aim of contributing to reductions in the carbon footprint.
- Research challenge: Development of technological solutions for improved accuracy of estimations regarding required quantities of food/products at each stage of the supply chain.
- Research challenge: Development of technological solutions for the analysis of pricing disparities, ultimately working towards greater affordability and accessibility.
- Research challenge: Development of solutions/methods for incorporating technology in education and societal restructuring towards a greener and more sustainable society.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Mahumud, R.A.; Sahle, B.W.; Owusu-Addo, E.; Chen, W.; Morton, R.L.; Renzaho, A.M.N. Association of Dietary Intake, Physical Activity, and Sedentary Behaviours with Overweight and Obesity among 282,213 Adolescents in 89 Low and Middle Income to High-Income Countries. Int. J. Obes. 2021, 45, 2404–2418. [Google Scholar] [CrossRef]
- Blüher, M. Obesity: Global Epidemiology and Pathogenesis. Nat. Rev. Endocrinol. 2019, 15, 288–298. [Google Scholar] [CrossRef] [PubMed]
- Hammond, R.A.; Dubé, L. A Systems Science Perspective and Transdisciplinary Models for Food and Nutrition Security. Proc. Natl. Acad. Sci. USA 2012, 109, 12356–12363. [Google Scholar] [CrossRef]
- World Health Organization. Available online: https://www.who.int/data/gho/indicator-metadata-registry/imr-details/3420 (accessed on 4 March 2024).
- Mariadoss, S.; Augustin, F. Enhanced Sugeno Fuzzy Inference System with Fuzzy AHP and Coefficient of Variation to Diagnose Cardiovascular Disease during Pregnancy. J. King Saud Univ. Comput. Inf. Sci. 2023, 35, 101659. [Google Scholar] [CrossRef]
- Betts, J.A.; Gonzalez, J.T. Personalised Nutrition: What Makes You So Special? Nutr. Bull. 2016, 41, 353–359. [Google Scholar] [CrossRef]
- Mathers, J.C. Paving the Way to Better Population Health through Personalised Nutrition. EFS2 2019, 17, e170713. [Google Scholar] [CrossRef]
- Di Renzo, L.; Gualtieri, P.; Romano, L.; Marrone, G.; Noce, A.; Pujia, A.; Perrone, M.A.; Aiello, V.; Colica, C.; De Lorenzo, A. Role of Personalized Nutrition in Chronic-Degenerative Diseases. Nutrients 2019, 11, 1707. [Google Scholar] [CrossRef]
- Konstantinidou, V.; Ruiz, L.A.D.; Ordovás, J.M. Personalized Nutrition and Cardiovascular Disease Prevention: From Framingham to PREDIMED. Adv. Nutr. 2014, 5, 368S–371S. [Google Scholar] [CrossRef] [PubMed]
- Wheatley, S.D.; Deakin, T.A.; Arjomandkhah, N.C.; Hollinrake, P.B.; Reeves, T.E. Low Carbohydrate Dietary Approaches for People with Type 2 Diabetes—A Narrative Review. Front. Nutr. 2021, 8, 687658. [Google Scholar] [CrossRef]
- Shastri, A.A.; Lombardo, J.; Okere, S.C.; Higgins, S.; Smith, B.C.; DeAngelis, T.; Palagani, A.; Hines, K.; Monti, D.A.; Volpe, S.; et al. Personalized Nutrition as a Key Contributor to Improving Radiation Response in Breast Cancer. Int. J. Mol. Sci. 2021, 23, 175. [Google Scholar] [CrossRef]
- Miyazawa, T.; Hiratsuka, Y.; Toda, M.; Hatakeyama, N.; Ozawa, H.; Abe, C.; Cheng, T.-Y.; Matsushima, Y.; Miyawaki, Y.; Ashida, K.; et al. Artificial Intelligence in Food Science and Nutrition: A Narrative Review. Nutr. Rev. 2022, 80, 2288–2300. [Google Scholar] [CrossRef] [PubMed]
- Sood, S.; Singh, H. Computer Vision and Machine Learning Based Approaches for Food Security: A Review. Multimed. Tools Appl. 2021, 80, 27973–27999. [Google Scholar] [CrossRef]
- Trabucchi, D.; Buganza, T. Data-Driven Innovation: Switching the Perspective on Big Data. EJIM 2019, 22, 23–40. [Google Scholar] [CrossRef]
- Theodoridis, T.; Solachidis, V.; Dimitropoulos, K.; Gymnopoulos, L.; Daras, P. A Survey on AI Nutrition Recommender Systems. In Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Rhodes, Greece, 5–7 June 2019; pp. 540–546. [Google Scholar] [CrossRef]
- Agrawal, K.; Kumar, N. Artificial Intelligence Innovations: Inception of New Horizons in Food Processing Sector. In Proceedings of the 2023 IEEE Silchar Subsection Conference (SILCON), Silchar, India, 3–5 November 2023; pp. 1–8. [Google Scholar] [CrossRef]
- Roy, D.; Dutta, M. A Survey on Personalized Health Recommender Systems for Diverse Healthcare Applications. In Proceedings of the 2023 4th International Conference on Computing and Communication Systems (I3CS), Shillong, India, 16–18 March 2023; pp. 1–9. [Google Scholar] [CrossRef]
- Sempionatto, J.R.; Montiel, V.R.-V.; Vargas, E.; Teymourian, H.; Wang, J. Wearable and Mobile Sensors for Personalized Nutrition. ACS Sens. 2021, 6, 1745–1760. [Google Scholar] [CrossRef]
- Wilson-Barnes, S.; Pongcharoenyong, L.; Gymopoulos, L.; Dimitropoulos, K.; Jaksic, M.; Mihajlović, A.; Brkic, B.; Lanham-New, S.; Hart, K. The Utility of Breath Volatile Organic Compound (VOC) Sampling as a Biomarker of Sub-Optimal Nutritional Status: A UK Pilot Study. Proc. Nutr. Soc. 2022, 81, E90. [Google Scholar] [CrossRef]
- Kolodziejczyk, A.A.; Zheng, D.; Elinav, E. Diet–Microbiota Interactions and Personalized Nutrition. Nat. Rev. Microbiol. 2019, 17, 742–753. [Google Scholar] [CrossRef]
- Van Ommen, B.; Van Den Broek, T.; De Hoogh, I.; Van Erk, M.; Van Someren, E.; Rouhani-Rankouhi, T.; Anthony, J.C.; Hogenelst, K.; Pasman, W.; Boorsma, A.; et al. Systems Biology of Personalized Nutrition. Nutr. Rev. 2017, 75, 579–599. [Google Scholar] [CrossRef]
- Ordovas, J.M.; Ferguson, L.R.; Tai, E.S.; Mathers, J.C. Personalised Nutrition and Health. BMJ 2018, 361, bmj.k2173. [Google Scholar] [CrossRef]
- Michel, M.; Burbidge, A. Nutrition in the Digital Age—How Digital Tools Can Help to Solve the Personalized Nutrition Conundrum. Trends Food Sci. Technol. 2019, 90, 194–200. [Google Scholar] [CrossRef]
- Laddu, D.; Hauser, M. Addressing the Nutritional Phenotype Through Personalized Nutrition for Chronic Disease Prevention and Management. Prog. Cardiovasc. Dis. 2019, 62, 9–14. [Google Scholar] [CrossRef]
- Beura, S.; Kundu, P.; Das, A.K.; Ghosh, A. Metagenome-Scale Community Metabolic Modelling for Understanding the Role of Gut Microbiota in Human Health. Comput. Biol. Med. 2022, 149, 105997. [Google Scholar] [CrossRef] [PubMed]
- Amorim, D.; Miranda, F.; Ferreira, L.; Abreu, C. Data-Driven Carbohydrate Counting Accuracy Monitoring: A Personalized Approach. Procedia Comput. Sci. 2022, 204, 900–906. [Google Scholar] [CrossRef]
- Greenberg, K.L.; Poupko, T.; Sorotzkin, D.; Keidar, O.; Zwas, D.R. Development and Usage of a Health Recommendation Web Tool (HeaRT) Designed to Inform Women of Personalized Preventive Health Recommendations. Internet Interv. 2023, 31, 100599. [Google Scholar] [CrossRef] [PubMed]
- Stefanidis, K.; Tsatsou, D.; Konstantinidis, D.; Gymnopoulos, L.; Daras, P.; Wilson-Barnes, S.; Hart, K.; Cornelissen, V.; Decorte, E.; Lalama, E.; et al. PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets. Nutrients 2022, 14, 4435. [Google Scholar] [CrossRef]
- Yang, L.; Hsieh, C.-K.; Yang, H.; Pollak, J.P.; Dell, N.; Belongie, S.; Cole, C.; Estrin, D. Yum-Me: A Personalized Nutrient-Based Meal Recommender System. ACM Trans. Inf. Syst. 2018, 36, 1–31. [Google Scholar] [CrossRef]
- Harvey, M.; Elsweiler, D. Automated Recommendation of Healthy, Personalised Meal Plans. In Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Austria, 16–19 September 2015; pp. 327–328. [Google Scholar] [CrossRef]
- Livingstone, K.M.; Ramos-Lopez, O.; Pérusse, L.; Kato, H.; Ordovas, J.M.; Martínez, J.A. Precision Nutrition: A Review of Current Approaches and Future Endeavors. Trends Food Sci. Technol. 2022, 128, 253–264. [Google Scholar] [CrossRef]
- Kirk, D.; Catal, C.; Tekinerdogan, B. Precision Nutrition: A Systematic Literature Review. Comput. Biol. Med. 2021, 133, 104365. [Google Scholar] [CrossRef]
- Page, M.J.; Moher, D.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews. BMJ 2021, 372, n160. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Haddaway, N.R.; Page, M.J.; Pritchard, C.C.; McGuinness, L.A. PRISMA2020: An R Package and Shiny App for Producing PRISMA 2020-Compliant Flow Diagrams, with Interactivity for Optimised Digital Transparency and Open Synthesis. Campbell Syst. Rev. 2022, 18, e1230. [Google Scholar] [CrossRef]
- Haseena, S.; Saroja, S.; Revathi, T. A Fuzzy Approach for Multi Criteria Decision Making in Diet Plan Ranking System Using Cuckoo Optimization. Neural Comput. Appl. 2022, 34, 13625–13638. [Google Scholar] [CrossRef]
- Lakshmi, M.J.; Niharika, G.; Kodipalli, A.; Rohini, B.R.; Gargi, N.; Soma, B. Revolutionizing Personalized Nutrition Using F-AHP, Fuzzy TOPSIS and Multicriteria Selection Analysis. In Proceedings of the 2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET), B G Nagara, India, 23–24 November 2023; pp. 1–6. [Google Scholar] [CrossRef]
- Zhang, J.; Li, M.; Liu, W.; Lauria, S.; Liu, X. Many-Objective Optimization Meets Recommendation Systems: A Food Recommendation Scenario. Neurocomputing 2022, 503, 109–117. [Google Scholar] [CrossRef]
- Salloum, G.; Tekli, J. Automated and Personalized Meal Plan Generation and Relevance Scoring Using a Multi-Factor Adaptation of the Transportation Problem. Soft Comput. 2022, 26, 2561–2585. [Google Scholar] [CrossRef]
- Rout, A.K.; Sethy, A.; Mouli, N.S. Machine Learning Model for Awareness of Diet Recommendation. In Proceedings of the 2023 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, 26–28 April 2023; pp. 96–101. [Google Scholar] [CrossRef]
- Kaur, R.; Kumar, R.; Gupta, M. Food Image-Based Diet Recommendation Framework to Overcome PCOS Problem in Women Using Deep Convolutional Neural Network. Comput. Electr. Eng. 2022, 103, 108298. [Google Scholar] [CrossRef]
- Aguilar, E.; Nagarajan, B.; Remeseiro, B.; Radeva, P. Bayesian Deep Learning for Semantic Segmentation of Food Images. Comput. Electr. Eng. 2022, 103, 108380. [Google Scholar] [CrossRef]
- Romero-Tapiador, S.; Tolosana, R.; Morales, A.; Fierrez, J.; Vera-Rodriguez, R.; Espinosa-Salinas, I.; Freixer, G.; De Santa Pau, E.C.; De Molina, A.R.; Ortega-Garcia, J. AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours. IEEE Access 2023, 11, 112199–112211. [Google Scholar] [CrossRef]
- Azzimani, K.; Bihri, H.; Dahmi, A.; Azzouzi, S.; Charaf, M.E.H. An AI Based Approach for Personalized Nutrition and Food Menu Planning. In Proceedings of the 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), Fez, Morocco, 1–2 December 2022; pp. 1–5. [Google Scholar] [CrossRef]
- Shandilya, R.; Sharma, S.; Wong, J. MATURE-Food: Food Recommender System for MAndatory FeaTURE Choices A System for Enabling Digital Health. Int. J. Inf. Manag. Data Insights 2022, 2, 100090. [Google Scholar] [CrossRef]
- Xu, Z.; Li, J.; Topaz, M. ElCombo: Knowledge-Based Personalized Meal Recommendation System for Chinese Community-Dwelling Elders. In Proceedings of the 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI), Houston, TX, USA, 26–29 June 2023; pp. 478–479. [Google Scholar] [CrossRef]
- Hosen, M.A.; Moz, S.H.; Kabir, S.k.S.; Galib, D.r.S.M.d.; Adnan, D.r.M.d.N. Enhancing Thyroid Patient Dietary Management with an Optimized Recommender System Based on PSO and K-Means. Procedia Comput. Sci. 2023, 230, 688–697. [Google Scholar] [CrossRef]
- Larizza, C.; Bosoni, P.; Quaglini, S.; Chasseur, M.; Bevolo, V.; Zuccotti, G.; Calcaterra, V. V-Care: An Application to Support Lifestyle Improvement in Children with Obesity. Int. J. Med. Inform. 2023, 177, 105140. [Google Scholar] [CrossRef]
- Lodhi, A.B.; Bilal, M.A.; Bilal, H.S.M.; Khan, K.U.; Satti, F.A.; Khalid, S.; Lee, S. PNRG: Knowledge Graph-Driven Methodology for Personalized Nutritional Recommendation Generation. In Digital Health Transformation, Smart Ageing, and Managing Disability; Jongbae, K., Mokhtari, M., Aloulou, H., Abdulrazak, B., Seungbok, L., Eds.; Lecture Notes in Computer Science; Springer Nature: Cham, Switzerland, 2023; Volume 14237, pp. 230–238. ISBN 978-3-031-43949-0. [Google Scholar] [CrossRef]
- Burgermaster, M.; Desai, P.M.; Heitkemper, E.M.; Juul, F.; Mitchell, E.G.; Turchioe, M.; Albers, D.J.; Levine, M.E.; Larson, D.; Mamykina, L. Who Needs What (Features) When? Personalizing Engagement with Data-Driven Self-Management to Improve Health Equity. J. Biomed. Inform. 2023, 144, 104419. [Google Scholar] [CrossRef]
- Islam, T.; Joyita, A.R.; Alam, M.G.R.; Hassan, M.M.; Hassan, M.R.; Gravina, R. Human Behavior-Based Personalized Meal Recommendation and Menu Planning Social System. IEEE Trans. Comput. Soc. Syst. 2023, 10, 2099–2110. [Google Scholar] [CrossRef]
- Yang, J. Tailored Nutrition Service to Reduce the Risk of Chronic Diseases. In Health Information Science; Traina, A., Wang, H., Zhang, Y., Siuly, S., Zhou, R., Chen, L., Eds.; Lecture Notes in Computer Science; Springer Nature: Cham, Switzerland, 2022; Volume 13705, pp. 64–75. ISBN 978-3-031-20626-9. [Google Scholar] [CrossRef]
- Fu, C.; Huang, Z.; Van Harmelen, F.; He, T.; Jiang, X. Food4healthKG: Knowledge Graphs for Food Recommendations Based on Gut Microbiota and Mental Health. Artif. Intell. Med. 2023, 145, 102677. [Google Scholar] [CrossRef] [PubMed]
- Yang, J. Using Nutrigenomics to Guide Personalized Nutrition Supplementation for Bolstering Immune System. Health Inf. Sci. Syst. 2023, 11, 4. [Google Scholar] [CrossRef]
- Yang, J. Genetic Data Analysis and Business Process Management Platform for Personalized Nutrition Service. In Computational Science and Its Applications—ICCSA 2022 Workshops; Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2022; Volume 13381, pp. 550–559. ISBN 978-3-031-10547-0. [Google Scholar] [CrossRef]
- Geng, S.; He, X.; Liang, G.; Niu, B.; Liu, S.; He, Y. Accuracy-Diversity Optimization in Personalized Recommender System via Trajectory Reinforcement Based Bacterial Colony Optimization. Inf. Process. Manag. 2023, 60, 103205. [Google Scholar] [CrossRef]
- Sahal, R.; Alsamhi, S.H.; Brown, K.N. Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry. Sensors 2022, 22, 5918. [Google Scholar] [CrossRef]
- Chivukula, R.; Lakshmi, T.J.; Sumalatha, S.; Reddy, K.L.R. Ontology Based Food Recommendation. In IOT with Smart Systems; Senjyu, T., Mahalle, P., Perumal, T., Joshi, A., Eds.; Smart Innovation, Systems and Technologies; Springer Nature: Singapore, 2022; Volume 251, pp. 751–759. ISBN 9789811639449. [Google Scholar] [CrossRef]
- Kaur, R.; Jain, M.; McAdams, R.M.; Sun, Y.; Gupta, S.; Mutharaju, R.; Cho, S.J.; Saluja, S.; Palma, J.P.; Kaur, A.; et al. An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit. IEEE Access 2023, 11, 142433–142446. [Google Scholar] [CrossRef]
- Martinho, D.; Crista, V.; Karakaya, Z.; Gamechi, Z.; Freitas, A.; Neves, J.; Novais, P.; Marreiros, G. Design and Development of Ontology for AI-Based Software Systems to Manage the Food Intake and Energy Consumption of Obesity, Diabetes and Tube Feeding Patients. In Progress in Artificial Intelligence; Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R., Eds.; Lecture Notes in Computer Science; Springer Nature: Cham, Switzerland, 2023; Volume 14115, pp. 29–40. ISBN 978-3-031-49007-1. [Google Scholar] [CrossRef]
- Rostami, M.; Berahmand, K.; Forouzandeh, S.; Ahmadian, S.; Farrahi, V.; Oussalah, M. A Novel Healthy Food Recommendation to User Groups Based on a Deep Social Community Detection Approach. Neurocomputing 2024, 576, 127326. [Google Scholar] [CrossRef]
- Palacios, C.; Bolton, J.; Wang, W.; Gatto, A.; Concepción, L.; Sadjadi, M.; Varella, M.; Hannan, J. Development and Pilot Testing of the Baby-Feed Web Application for Healthcare Professionals and Parents to Improve Infant Diets. Int. J. Med. Inform. 2023, 174, 105047. [Google Scholar] [CrossRef]
- Wang, X.; Li, Z.; Wu, H. Personalized Recommendation Method of “Carbohydrate-Protein” Supplement Based on Machine Learning and Enumeration Method. IEEE Access 2023, 11, 100573–100586. [Google Scholar] [CrossRef]
- Cunha, C.; Duarte, P.; Oliveira, R. Nutrition Control System Based on Short-Term Personal Demands. Procedia Comput. Sci. 2023, 224, 565–571. [Google Scholar] [CrossRef]
- Neha, K.; Sanjan, P.; Hariharan, S.; Namitha, S.; Jyoshna, A.; Prasad, A.B. Food Prediction Based on Recipe Using Machine Learning Algorithms. In Proceedings of the 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India, 23–25 August 2023; pp. 411–416. [Google Scholar] [CrossRef]
- Wang, S.; Xia, K.; Yang, Y.; Qiu, R.; Qi, Y.; Miao, Q.; Xie, W.; Liu, T. A Recommender System for Healthy Food Choices Based on Integer Programming. In Advances in Transdisciplinary Engineering; Chen, C.-H., Scapellato, A., Barbiero, A., Korzun, D.G., Eds.; IOS Press: Amsterdam, The Netherlands, 2022; ISBN 978-1-64368-352-2. [Google Scholar] [CrossRef]
- Buzcu, B.; Varadhajaran, V.; Tchappi, I.; Najjar, A.; Calvaresi, D.; Aydoğan, R. Explanation-Based Negotiation Protocol for Nutrition Virtual Coaching. In PRIMA 2022: Principles and Practice of Multi-Agent Systems; Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2023; Volume 13753, pp. 20–36. ISBN 978-3-031-21202-4. [Google Scholar] [CrossRef]
- Shubhashree, L.A.; Chaudhari, S.; Aparna, R. A Nutrition-Based Smart Recipe Recommender for Healthy Living. In Proceedings of the 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), Bangalore, India, 7–9 October 2022; pp. 1–6. [Google Scholar] [CrossRef]
- Ribeiro, D.; Barbosa, T.; Ribeiro, J.; Sousa, F.; Vieira, E.F.; Silva, M.; Silva, A. SousChef System for Personalized Meal Recommendations: A Validation Study. Appl. Sci. 2022, 12, 702. [Google Scholar] [CrossRef]
- Wu, H.; Chen, X.; Li, X.; Ma, H.; Zheng, Y.; Li, X.; Meng, X.; Meng, L. A Visually-Aware Food Analysis System for Diet Management. In Proceedings of the 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Taipei City, Taiwan, 18–22 July 2022; p. 1. [Google Scholar] [CrossRef]
- Forouzandeh, S.; Rostami, M.; Berahmand, K.; Sheikhpour, R. Health-Aware Food Recommendation System with Dual Attention in Heterogeneous Graphs. Comput. Biol. Med. 2024, 169, 107882. [Google Scholar] [CrossRef] [PubMed]
- RahmathNisha, S.; Aarthi, M.; Shyamala, C.; Pavithra, M.; Vallipriyadharshini, K.; Raj, B.S. Intelligent Nutrition Assistant Application. In Proceedings of the 2023 12th International Conference on Advanced Computing (ICoAC), Chennai, India, 17–19 August 2023; pp. 1–6. [Google Scholar]
- Yera, R.; Alzahrani, A.A.; Martínez, L. Exploring Post-Hoc Agnostic Models for Explainable Cooking Recipe Recommendations. Knowl. Based Syst. 2022, 251, 109216. [Google Scholar] [CrossRef]
- Li, D.; Zaki, M.J.; Chen, C. Health-Guided Recipe Recommendation over Knowledge Graphs. J. Web Semant. 2023, 75, 100743. [Google Scholar] [CrossRef]
- Kansaksiri, P.; Panomkhet, P.; Tantisuwichwong, N. Smart Cuisine: Generative Recipe & ChatGPT Powered Nutrition Assistance for Sustainable Cooking. Procedia Comput. Sci. 2023, 225, 2028–2036. [Google Scholar] [CrossRef]
- Safitri, S.; Mantoro, T.; Bhakti, M.A.C.; Wandy, W. Cooking and Food Information Chatbot System Using GPT-3. In Proceedings of the 2023 IEEE 9th International Conference on Computing, Engineering and Design (ICCED), Kuala Lumpur, Malaysia, 7–8 November 2023; pp. 1–6. [Google Scholar] [CrossRef]
- Hasan, W.U.; Tuz Zaman, K.; Zadeh, M.S.A.T.; Li, J. Eat This, Not That!—A Personalised Restaurant Menu Decoder That Helps You Pick the Right Food. In Proceedings of the 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom), Genoa, Italy, 17–19 October 2022; pp. 43–48. [Google Scholar] [CrossRef]
- Takahashi, C.; Matsushita, M.; Yamanishi, R. Exploration Cycle Finding a Better Dining Experience: A Framework of Meal-Plates. Procedia Comput. Sci. 2023, 225, 2902–2911. [Google Scholar] [CrossRef]
- Sonkusale, S. Smart Threads for Tissue-Embedded Bioelectronics. In Proceedings of the 2022 IEEE Custom Integrated Circuits Conference (CICC), Newport Beach, CA, USA, 24–27April 2022; pp. 1–7. [Google Scholar] [CrossRef]
- Ates, H.C.; Nguyen, P.Q.; Gonzalez-Macia, L.; Morales-Narváez, E.; Güder, F.; Collins, J.J.; Dincer, C. End-to-End Design of Wearable Sensors. Nat. Rev. Mater. 2022, 7, 887–907. [Google Scholar] [CrossRef]
- Migliorelli, C.; Ros-Freixedes, L.; Gomez-Martinez, M.; Sistach-Bosch, L.; Orte, S. Carpediem: Investigating the Interactions of Health Pillars to Design Holistic Recommendations for Achieving Long-Term Changes in Lifestyle Behaviours. In Intelligent Computing; Arai, K., Ed.; Lecture Notes in Networks and Systems; Springer Nature: Cham, Switzerland, 2023; Volume 711, pp. 1378–1395. ISBN 978-3-031-37716-7. [Google Scholar] [CrossRef]
- Wang, M.; Yang, Y.; Min, J.; Song, Y.; Tu, J.; Mukasa, D.; Ye, C.; Xu, C.; Heflin, N.; McCune, J.S.; et al. A Wearable Electrochemical Biosensor for the Monitoring of Metabolites and Nutrients. Nat. Biomed. Eng. 2022, 6, 1225–1235. [Google Scholar] [CrossRef]
- Khan, M.I.; Acharya, B.; Chaurasiya, R.K. iHearken: Chewing Sound Signal Analysis Based Food Intake Recognition System Using Bi-LSTM Softmax Network. Comput. Methods Programs Biomed. 2022, 221, 106843. [Google Scholar] [CrossRef]
- Xiao-Yong, C.; Bo-Xiong, Y.; Shuai, Z.; Jie, D.; Peng, S.; Lin, G. Intelligent Health Management Based on Analysis of Big Data Collected by Wearable Smart Watch. Cogn. Robot. 2023, 3, 1–7. [Google Scholar] [CrossRef]
- Rivera-Romero, O.; Gabarron, E.; Ropero, J.; Denecke, K. Designing Personalised mHealth Solutions: An Overview. J. Biomed. Inform. 2023, 146, 104500. [Google Scholar] [CrossRef] [PubMed]
- Zamanillo-Campos, R.; Fiol-deRoque, M.A.; Serrano-Ripoll, M.J.; Mira-Martínez, S.; Ricci-Cabello, I. Development and Evaluation of DiabeText, a Personalized mHealth Intervention to Support Medication Adherence and Lifestyle Change Behaviour in Patients with Type 2 Diabetes in Spain: A Mixed-Methods Phase II Pragmatic Randomized Controlled Clinical Trial. Int. J. Med. Inform. 2023, 176, 105103. [Google Scholar] [CrossRef] [PubMed]
- Martínez-Rodríguez, A.; Martínez-Olcina, M.; Mora, J.; Navarro, P.; Caturla, N.; Jones, J. New App-Based Dietary and Lifestyle Intervention on Weight Loss and Cardiovascular Health. Sensors 2022, 22, 768. [Google Scholar] [CrossRef] [PubMed]
- Al-Rayes, S.; Al Yaqoub, F.A.; Alfayez, A.; Alsalman, D.; Alanezi, F.; Alyousef, S.; AlNujaidi, H.; Al-Saif, A.K.; Attar, R.; Aljabri, D.; et al. Gaming Elements, Applications, and Challenges of Gamification in Healthcare. Inform. Med. Unlocked 2022, 31, 100974. [Google Scholar] [CrossRef]
- Oc, Y.; Plangger, K. GIST Do It! How Motivational Mechanisms Help Wearable Users Develop Healthy Habits. Comput. Hum. Behav. 2022, 128, 107089. [Google Scholar] [CrossRef]
- Andres, F. Data Engineering Challenges in Intelligent Food and Cooking Recipes. In Proceedings of the 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW), Anaheim, CA, USA, 3–7 April 2023; pp. 214–217. [Google Scholar] [CrossRef]
- Sedrakyan, G.; Gavai, A.; Van Hillegersberg, J. Design Implications towards Human-Centric Semantic Recommenders for Sustainable Food Consumption. In Advances in Conceptual Modeling; Sales, T.P., Araújo, J., Borbinha, J., Guizzardi, G., Eds.; Lecture Notes in Computer Science; Springer Nature: Cham, Switzerland, 2023; Volume 14319, pp. 312–328. ISBN 978-3-031-47111-7. [Google Scholar] [CrossRef]
- European Commission. Directorate General for Research and Innovation. In Food 2030: Pathways for Action 2.0: R&I Policy as a Driver for Sustainable, Healthy, Climate Resilient and Inclusive Food Systems; European Commission: Luxembourg, 2023. [Google Scholar] [CrossRef]
Reference No. | Method/Model/Technology | Datasets | Input | Platform | Note |
---|---|---|---|---|---|
A. Nutrition Recommendation Systems | |||||
Haseena et al. (2022) [36] | Cuckoo (optimization) Fuzzy AHP (multi-criteria) Fuzzy TOPSIS (decision making) * | - | Physiological data (use the data as values for the comparison matrix) | Questionnaire | * |
Lakshmi et al. (2023) [37] | Fuzzy AHP (multi-criteria) Fuzzy TOPSIS (decision making) | - | Physiological data, dietary data, health data (use the data as values for the comparison matrix) | - | * |
Zhang et al. (2022) [38] | MaOO (optimization) * | MyFitnessPal | Dietary data, nutrition values | - | * |
Salloum et al. (2022) [39] | TOP (optimization) * | - | Physiological data (use a loss function) | Questionnaire | * |
Rout et al. (2023) [40] | KNN (clustering) RF (classification) * | Kaggle (calorie dataset) | Nutrition values, physical activity (clustering the data) | Web application | * |
Kaur et al. (2022) [41] | EfficientNet (B0-B7) (classification) VGG16 (classification) VGG19 (classification) ResNet50 (classification) ResNet100 (classification) * | Food101 | Physiological data, RBG images (compute BMI and caloric needs from physiological data and use food images to calculate caloric income and what to recommend) | Web application | * |
Aguilar et al. (2022) [42] | Bayesian network (probabilistic modelling) * | UECFOODPIX, UNIMIB2016, Food201 | RGB images | - | * |
Romero-Tapiador et al. (2023) [43] | CNN (classification) * | food images (AI4Food-NutritionDB) | Physiological data, preferences, physical activity, food images (construct the user profile from all the above data and then create food image datasets with different eating behaviours) | Mobile application | * |
Azzimani et al. (2022) [44] | SVM (classification) MFCN (segmentation) * | Morocco FCT | Physiological data, health data, RGBD images (construct the user profile from the physiological data and health data and then include the information of food image) | - | * |
Shandilya et al. (2022) [45] | Content-based recommendation system (recommendation system) * | CKD, USDA | Health data, preferences, rating (item feature-based classification, then extract mandatory features from the user’s profile and finally with the extraction of the preferred features they generate recommendations) | - | * |
Xu et al. (2023) [46] | KG (reasoning) NLP (natural language) * | User Profiles, Food Dataset | Sociodemographic, nutrition and health, dietary preferences (rule-based relation among the KG schema) | - | * |
Hosen el. al. (2023) [47] | PSO (optimization) k-means (clustering) SOM (clustering) NLP (natural language) * | American food chart | Dietary data, health data, contextual info (clustering the data) | - | * |
Larizza et al. (2023) [48] | - | Use their own DB | Demographics, physiological data, lifestyle (construct child profile using the above data) | Questionnaires | * |
Lodhi et al. (2023) [49] | KG (reasoning) Ontology (reasoning) * | - | Health data, demographics (construct user profile with the data and then extract proper nutritional recommendations based on rule-based and data-driven approaches) | Web application | * |
Burgermaster et al. (2023) [50] | - | - | Meal logs, health data, food images (these data are for constructing the user profile) | Mobile application | * |
Islam et al. (2023) [51] | TOPSIS (decision making) * | - | EEG signals, food nutritional (extract features from EEG collection and survey data and then recommend foods and menus) | Questionnaires | * |
Yang et al. (2022) [52] | - | - | Genetic data, physical data, diet style, habits, medical data (construct user profile with the above data) | Mobile application, Questionnaire | ** |
Fu et al. (2023) [53] | KG (reasoning) Ontology (reasoning) * | FoodData Central dataset (FDC), FoodOn, Chinese Food Ontology, KEGG, MENDA, MiKG, MeSH | Health data, food, gut microbiota data (the KG works with queries as inputs and returns the relationship among the above three categories) | - | * |
Yang et al. (2023) [54] | - | - | Health data, physiological data (construct the user profile) | Mobile application, Questionnaire | ** |
Yang et al. (2022) [55] | LIMS (data management processes) Bioinformatic pipelines Genetic Interpretation System, CRM * | AutDB, DisGeNET, OMIM | Physiological data (construct the user profile) | Mobile application, Questionnaire | ** |
Geng et.al. (2023) [56] | Heuristic Optimization, TRBCO * | Shaffer, Fonseca, Kursawe, Poloni, ZDT1-6, Movielens-1M | Ratings (use of rating to recommend a meal) | - | * |
Sahal et al. (2022) [57] | DT | - | Dietary data, physical activity, contextual information (they do not mention how they integrate these data) | - | * |
Chivukula et al. (2022) [58] | Ontology * | - | (The ontology works with queries as inputs and returns the relationship among the tis classes) | - | |
Kaur et al. (2023) [59] | Ontology * | Clinical data | Clinical data, weight, gestational age (SPARQL queries to the ontology using the above data) | - | * |
Martinho et al. (2023) [60] | Ontology | FoodOn, Joint Food Ontology Workgroup (JFOW) | Preferences, allergies, meal intake, demographics (Queries to the ontology using the above data) | Mobile application, Web application | * |
Rostami et al. (2024) [61] | Clustering (encoder and decoder), deep neural networks * | - | Preferences, health factors of foods (construction of a user-rating matrix with the above data) | - | * |
Palacios et al. (2023) [62] | ADDIE * | - | Food frequency (construct user profile and feed it to the model) | Questionnaire Web application | * |
Wang et al. (2023) [63] | BP Neural Network Model, Gradient Boosted Regression Trees (GBRT) * | - | Health data, physiological data, physical activity, contextual information (input the above data into the models and generate personalized advice) | - | * |
Cunha et al. (2023) [64] | RNN, LSTM, GRU * | FitBit fitness tracker data | Food intake, physical activity, physiological data (input the above data into the models to predict BMI, weight, muscle mass, etc.) | - | * |
B. Recipe Recommendation Systems | |||||
Neha et al. (2023) [65] | CNN, Naive Bayes, Fuzzy Rule * | - | Text data of ingredients and recipes (input of the above information to the models) | - | * |
Wang et al. (2022) [66] | Integer programming | - | Physiological data, health data, preferences (input of the above data to the model) | Web application | * |
Buzcu et al. (2023) [67] | Ontology | OWL-based ontology | Allergies, preferences, type of cuisine (queries to the ontology using the above data) | Web application | * |
Shubhashree et al. (2022) [68] | KNN, Euclidean * | - | Physiological data, dietary data, preferences, restrictions (construct user profile with the above data and feed them to the model) | Web application | * |
Ribeiro et al. (2022) [69] | MaOO * | - | Physiological data, food type, allergies (construct user profile using the above data and feed them to the model) | Mobile application | * |
Wu et al. (2022) [70] | ATNet, PiNet | Created their own DB | Food images (input the above data to the model to classify them) | Web application | |
Forouzandeh et al. (2024) [71] | NLA, SLA, GAT, GNN * | Allrecipes | Rating, recipes (combination of user profiles and ratings of recipes to produce healthy recipes recommendations) | - | |
RahmathNisha et al. (2023) [72] | Decision trees, KNN, and SVM * | Kaggle (food and nutrition) | Physiological data, food image (physiological data re used to construct user profile and food images to extract features and then recommend a food) | Web application | * |
Yera et al. (2022) [73] | KG * | Coolpod, Allrecipe, Yammly, USDA, Created their own DB | (This model does not use any inputs) | - | |
Li et al. (2023) [74] | KG * | Food.com, Food KG | Health data, preference data (two KGs are used for each data to extract features and then combine these outputs) | - | * |
Kansaksiri et al. (2023) [75] | Meta-AI, NLP * | Recipe1M | Food images, OpenAI-powered chat service (input food images into the model to extract ingredients) | - | |
Safitri et al. (2023) [76] | GPT-3, NLP * | Created their own DB | Contextual information (user texts with the chat-bot) | Desktop application | |
C. Restaurant Recommendation Systems | |||||
Hasan et al. (2022) [77] | AI * | Dataset of restaurant menus | Preferences, menu image (menu item extraction from menu image and combined with preferences to recommend a menu) | Web application | * |
Takahashi et al. (2023) [78] | Flow graph, CRF | Cookpad | Preferences (This model does not take any data as input. It is a flow graph which is being trained by the user and leads the user to preferred recommendations) | - |
Reference No. | Sensor/Device Used | Method/Model Input (Sensor Output Data) | Method/Model Scope |
---|---|---|---|
Wilson-Barnes et al. (2022) [19] | VOC | Human breath | Nutrient estimation |
Aguilar et al. (2022) [42] | Camera | RGB of a plate | Food recognition |
Azzimani et al. (2022) [44] | Camera | RGBD of a meal | Meal personalisation |
Islam et al. (2023) [51] | EEG | EEG signals | Affects of different meals |
Yang et al. (2022) [52] | Mobile | Genetic testing, physical examination, diet style, habits and customs, medical history, exercise data | Tailored nutrition solution |
Yang et al. (2023) [54] | Mobile, DNA kit | Lifestyle questionnaire, physical examination results, DNA data | Evaluating users’ immune status, nutritional deficiency risk |
Yang et al. (2022) [55] | Mobile, DNA kit | Analysing genetic data, lifestyle data, physical examination data | Genetic interpretation report, personalized nutrition report, customized nutrition packs |
Cunha et al. (2024) [64] | Food scale, body scale, smartwatches | Food intake attributes, physical activity metrics, body parameters | BMI prediction, personalised feedback, goal monitoring |
Ribeiro et al. (2022) [69] | Mobile | Food preferences, restrictions, nutritional needs | Meal recommendation system |
Wu et al. (2022) [70] | Camera | RGB of a meal | Food classification |
Migliorelli et al. (2023) [81] | Activity tracker | Step counter, physical activity, pulse, sleep hours and sleeping efficiency | Physical activities, cardiovascular activities, sleep patterns, nutritional habits |
Wang et al. (2022) [82] | NutriTrek | Age, BMI | Health monitoring, precision nutrition |
Khan et al. (2022) [83] | Headphone-like | Chewing sounds | Food intake type |
Xiao-Yong et al. (2023) [84] | Smartwatches, mobile | Pulse, heart rate, blood oxygen | Health management |
Zamanillo-Campos et al. (2023) [86] | Mobile | Patient-elicited data | Tailored brief text |
Martínez-Rodríguez et al. (2022) [87] | Mobile, activity tracker | Blood pressure, body weight, water intake, fruits intake, vegetables intake, physical activity | Personalised reminders, behavioural tips, educational material, progress tracking |
Oc et al. (2022) [89] | Smartwatches, smart wristbands, mobile | Preferences | Gamification |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tsolakidis, D.; Gymnopoulos, L.P.; Dimitropoulos, K. Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review. Informatics 2024, 11, 62. https://doi.org/10.3390/informatics11030062
Tsolakidis D, Gymnopoulos LP, Dimitropoulos K. Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review. Informatics. 2024; 11(3):62. https://doi.org/10.3390/informatics11030062
Chicago/Turabian StyleTsolakidis, Dimitris, Lazaros P. Gymnopoulos, and Kosmas Dimitropoulos. 2024. "Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review" Informatics 11, no. 3: 62. https://doi.org/10.3390/informatics11030062