From Images to Insights: A Multimedia AI Framework for Intelligent Food Logging and Nutritional Health Management

Abstract

The increasing prevalence of diet-related chronic diseases like obesity, cardiovascular disorders, and diabetes demands the development of efficient dietary monitoring tools. Manual food diaries and calorie-counting apps are largely unreliable due to human error and nonadherence. This work presents a multimedia-based AI framework, 'From Images to Insights', that can conduct intelligent food logging and nutritional health management. It incorporates computer vision with speech recognition and NLP to identify foods automatically, estimate portions, and calculate nutrients from images, speech, and text modal inputs. The proposed model leverages deep learning architecture such as EfficientNet and YOLOv8 for image-based food recognition, while natural language understanding is used to extract dietary information from speech or text inputs. Experimental evaluation yielded an accuracy of above 92% in recognizing food and an error of ±10% in nutrient estimation. Further, this study discusses its integration with healthcare systems that may offer personalized diet recommendations and manage chronic diseases. The proposed model forms the basis of intelligent, AI-driven dietary monitoring and preventive healthcare. Keywords: Artificial Intelligence, Food Logging, Image Processing, Multimedia Systems, Nutrition Analysis, Mobile Health (mHealth).