Intelligent Personalized Food Recommendation System Based on a Semantic Sensor Web

Abstract

With changes in eating habits and lifestyles in Taiwan, the number of patients with a chronic disease is increasing, especially the number of those with hypertension, hyperglycemia, and hyperlipidemia. However, a Food Service Recommendation (FSR) system based on user clinical data and health records has not been investigated. This work proposes a novel Intelligent Personalized Food Service Recommendation System (IPFSRS), which contains a Vital Sensor Web Layer (VSWL), Semantic Medical Web Layer (SMWL), and Medical Service Presentation Layer (MSPL). The vital sensors in the VSWL can transfer user clinical data based on Sensor Web Enablement (SWE). The SMWL uses Rule-Based Reasoning (RBR) and Domain Ontologies (DOs) based on the Semantic Web (SW) to determine a user’s health status according to that user’s data from the VSWL. Furthermore, Bayesian Classification (BC) can be utilized to predict future health states of users. Finally, the FSR determines health states according to the current and future health states of users in the MSPL.