Prototype of a Nutritional Control Device Based on Computer Vision in the City of Arequipa

Abstract

The Pan American Health Organization reports that in Latin America, more than 28% of the population faces moderate or severe food insecurity, while nearly 58% of adults are overweight or obese. This research presents a proposal for a nutritional control prototype based on computer vision and embedded systems, implemented on a Raspberry Pi platform. The system integrates image capture, food classification algorithms, and weight sensors to estimate nutritional intake in a semi-automated manner, reducing dependence on manual recording of dietary information. The results of the cross-sectional study applied to young people between the ages of 12 and 24 in Arequipa show deficiencies in nutritional culture and food self-control practices, validating the relevance of the proposed device as a potential scalable engineering solution.