MedDietAgent: An AI-based Mobile App for Harmonizing Individuals' Dietary Choices with the Mediterranean Diet Pattern.

Fotios S Konstantakopoulos, Michail Sfakianos, Eleni I Georga, Konstantinos I Mavrokotas, Daphne N Katsarou, Konstantinos Chalatsis, Charalambos Zapadiotis, Anastasia Panousi, Sifis Plimakis, Sofia Eleftheriou, Anastasia Kanellou, Dimitrios I Fotiadis
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引用次数: 0

Abstract

Recently, there has been an increasing interest in applying technological advances to offer specific dietary recommendations in the field of nutrition and health. Dietary recommendation systems are advanced tools designed to assist individuals in making well-informed and health-conscious decisions on their food choices, taking into account their personal needs, preferences, and health targets or habits. In this study, we present an AI-based mobile app for harmonizing individuals' dietary choices with the pattern of the Mediterranean diet. A combination of computer vision, natural language processing, machine learning, and reinforcement techniques are used to record the nutritional information via images or speech and to generate dynamic recommendations tailored to the user's performance across key nutritional areas, encompassing calories, combined fats, proteins, carbohydrates, sugars, dietary fibers, sodium intake, fruits, vegetables, and dairy products. The image-based dietary assessment subsystem achieves a mean absolute percentage error of 3.73%, while the reinforcement learning subsystem achieves a 96% average reward. Then, a well-designed approach was taken to develop the MedDietAgent mobile app, using cutting-edge technologies and applying a simplistic approach. One of the key aspects of MedDietAgent is its ability to offer dynamic recommendations by monitoring the user's environment.

MedDietAgent:一个基于人工智能的移动应用程序,用于协调个人的饮食选择与地中海饮食模式。
最近,人们越来越有兴趣应用技术进步,在营养和健康领域提供具体的饮食建议。饮食推荐系统是一种先进的工具,旨在帮助个人在考虑到个人需求、偏好和健康目标或习惯的情况下,在食物选择方面做出知情和注重健康的决定。在这项研究中,我们提出了一个基于人工智能的移动应用程序,用于协调个人的饮食选择与地中海饮食模式。结合计算机视觉、自然语言处理、机器学习和强化技术,通过图像或语音记录营养信息,并根据用户在关键营养领域的表现生成动态建议,包括卡路里、组合脂肪、蛋白质、碳水化合物、糖、膳食纤维、钠摄入量、水果、蔬菜和乳制品。基于图像的膳食评估子系统的平均绝对百分比误差为3.73%,而强化学习子系统的平均奖励率为96%。然后,采用精心设计的方法开发MedDietAgent移动应用程序,使用尖端技术并采用简单的方法。MedDietAgent的一个关键方面是它能够通过监视用户的环境来提供动态建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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