The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation task.Clinical Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients.
Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera*
Di Luzio, Francesco Scotto;Tagliamonte, Nevio Luigi;Zollo, Loredana;
2023-01-01
Abstract
The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation task.Clinical Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
