Automated and Personalized Nutrition Health Assessment, Recommendation, and Progress Evaluation using Fuzzy Reasoning

Establishing a healthy lifestyle has become a very important aspect in people?s lives. The latter requires maintaining a healthy nutrition by considering the type and quantity of consumed foods. It also requires 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 to perform health assessment, whose services are costly, time consuming, and not readily available. While various e-nutrition solutions have been developed, yet most of them perform meal planning without performing health state assessment or evaluation (traditionally provided by human experts). To our knowledge, there is no existing automated solution to perform nutrition health assessment, recommendation, and progress evaluation, which are central pre-requites to the meal planning task. In this study, we introduce a novel framework titled PIN for Personalized Intelligent Nutrition recommendations. PIN relies on the fuzzy logic paradigm to simulate human expert health assessment capabilities, including weight, caloric intake, and exercise recommendations as well as progress evaluation and recommendation adjustments. It includes three essential and complementary modules: i) Weight Assessment and Recommendation (WAR), ii) Caloric Intake and Exercise Recommendation (CIER), and iii) Progress Evaluation and Recommendation Adjustment (PERA). This underlines the first computerized solution for nutrition health assessment. We have conducted a large battery of experiments involving 50 patient profiles and 11 nutrition expert evaluators to test the performance of PIN and evaluate its health assessment quality. Results show that PIN?s assessment and recommendations are on a par with and sometimes surpass those of human nutritionists.

Automated summary

The study introduces a novel framework titled PIN for Personalized Intelligent Nutrition recommendations, which relies on fuzzy logic paradigm to simulate human expert health assessment capabilities. Results from a large battery of experiments show that PIN's assessment and recommendations are on par with or even surpass those of human nutritionists.