Automated knowledge-based nutrition health assessment, recommendation, progress evaluation, and meal planning. (c2018)

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dc.contributor.author Salloum, George Kamil
dc.date.accessioned 2019-04-17T06:30:59Z
dc.date.available 2019-04-17T06:30:59Z
dc.date.copyright 2018 en_US
dc.date.submitted 2018-12-14
dc.identifier.uri http://hdl.handle.net/10725/10468
dc.description.abstract Establishing a healthy lifestyle has become a very important aspect in people’s lives. The latter requires maintaining a healthy nutrition by considering the nature and quantity of foods being consumed, as well as maintaining an active lifestyle including the necessary amount of physical exercise to regulate one’s intake and consumption of calories and nutrients. As a result, people reach out for nutrition experts which entails various obstacles: (i) the cost of seeking an expert’s help which is recurring and non-trivial, (ii) the time commitment required from a person to attend regular meetings with the expert, and (iii) the need for readily accessible health services which might be difficult to provide by a human expert. In this master thesis study, we design, implement, and evaluate a novel framework titled PIN: a computerized solution for Personalized Intelligent Nutrition recommendations. PIN consists of four main modules allowing four essential complementary functionalities: (i) Weight Assessment and Recommendation (WAR), (ii) Caloric Intake and Exercise Recommendation (CIER), (iii) Progress Evaluation and Recommendation Adjustment (PERA), and (iv) personalized Meal Plan Generation (MPG) and adaptation following patient chosen parameters (e.g., food preference, food compatibility, price, etc.). While most existing computerized solutions focus solely on the meal plan generation task, PIN provides the first full-fledged solution for nutrition health assessment, which results are required to run the meal planning task. It relies on the fuzzy logic paradigm to simulate human expert health assessment including weight, caloric intake, and exercise recommendations as well as progress evaluation and recommendation adjustments. PIN also provides a novel contribution in meal planning, introducing an adaptation of the transportation optimization problem to dynamically generate, change, adapt, and self-evaluate meal plans following the patient’s needs, compared with most existing meal planning solutions which fail to integrate all the different essential factors (meal-food compatibility, inner-food compatibility, preferences, diversity, and variety) while producing meal plans. We have conducted a large battery of experiments involving 50 patient profiles, 11 nutrition expert evaluators, and 5 non-expert testers, to test the performance of PIN, evaluating its health assessment and meal plan generation quality. Results highlight PIN’s assessment and recommendation qualities which are on a par with and sometimes surpass those of human nutrition experts. en_US
dc.language.iso en en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.subject Nutrition -- Evaluation en_US
dc.subject Nutrition -- Technological innovations en_US
dc.subject Nutrition -- Data processing en_US
dc.subject Nutrition -- Health aspects en_US
dc.subject Fuzzy logic en_US
dc.title Automated knowledge-based nutrition health assessment, recommendation, progress evaluation, and meal planning. (c2018) en_US
dc.type Thesis en_US
dc.term.submitted Fall en_US
dc.author.degree MS in Computer Engineering en_US
dc.author.school SOE en_US
dc.author.idnumber 201205401 en_US
dc.author.commembers Nakad, Zahi
dc.author.commembers Fawaz, Wissam
dc.author.department Electrical And Computer Engineering en_US
dc.description.embargo N/A en_US
dc.description.physdesc 1 hard copy: xvii, 214 leaves; ill. (chiefly col.); 31 cm. available at RNL. en_US
dc.author.advisor Tekli, Joe
dc.keywords Fuzzy Logic en_US
dc.keywords Fuzzy Agents en_US
dc.keywords Transportation Problem en_US
dc.keywords Shortest Path Computation en_US
dc.keywords Nutrition Health Assessment en_US
dc.keywords Meal Planning en_US
dc.keywords Food Graph en_US
dc.keywords Caloric Intake en_US
dc.keywords Body Fat Percentage en_US
dc.keywords Exercise Recommendation en_US
dc.keywords Progress Evaluation en_US
dc.keywords Parametric Model en_US
dc.description.bibliographiccitations Bibliography: leaves 189-194. en_US
dc.identifier.doi https://doi.org/10.26756/th.2019.116 en_US
dc.author.email george.salloum01@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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