NUTRIVISION: A System for Automatic Diet Management in Smart Healthcare
Madhumita Veeramreddy, Ashok Kumar Pradhan, Swetha Ghanta, Laavanya Rachakonda, Saraju P Mohanty
TL;DR
NutriVision addresses the need for personalized, real-time nutrition tracking by integrating image-based food recognition with portion estimation using a size reference and by incorporating user health data to tailor dietary guidance. The system leverages Faster R-CNN for robust food detection and maps identified foods to nutritional values, enabling automated nutrient estimation from images. It also enriches recommendations through NLP-based user profiles and collaborative filtering, delivered via an interactive chatbot and BMI-informed meal suggestions. Validation on a curated dataset shows strong classification performance and meaningful potential for real-time dietary management within smart healthcare, while noting challenges such as handling adjacent foods and the need for broader data diversity. Overall, NutriVision presents a practical, end-to-end solution that links visual dietary assessment with personalized health guidance to support healthier eating habits in real-world settings.
Abstract
Maintaining health and fitness through a balanced diet is essential for preventing non communicable diseases such as heart disease, diabetes, and cancer. NutriVision combines smart healthcare with computer vision and machine learning to address the challenges of nutrition and dietary management. This paper introduces a novel system that can identify food items, estimate quantities, and provide comprehensive nutritional information. NutriVision employs the Faster Region based Convolutional Neural Network, a deep learning algorithm that improves object detection by generating region proposals and then classifying those regions, making it highly effective for accurate and fast food identification even in complex and disorganized meal settings. Through smartphone based image capture, NutriVision delivers instant nutritional data, including macronutrient breakdown, calorie count, and micronutrient details. One of the standout features of NutriVision is its personalized nutritional analysis and diet recommendations, which are tailored to each user's dietary preferences, nutritional needs, and health history. By providing customized advice, NutriVision helps users achieve specific health and fitness goals, such as managing dietary restrictions or controlling weight. In addition to offering precise food detection and nutritional assessment, NutriVision supports smarter dietary decisions by integrating user data with recommendations that promote a balanced, healthful diet. This system presents a practical and advanced solution for nutrition management and has the potential to significantly influence how people approach their dietary choices, promoting healthier eating habits and overall well being. This paper discusses the design, performance evaluation, and prospective applications of the NutriVision system.