Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App

Abdelilah Nossair, Hamza El Housni
6/2/2024
cs.CV

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Abstract

The Smart Dietary Assistant utilizes Machine Learning to provide personalized dietary advice, focusing on users with conditions like diabetes. This app leverages the Grounding DINO model, which combines a text encoder and image backbone to enhance food item detection without requiring a labeled dataset. With an AP score of 52.5 on the COCO dataset, the model demonstrates high accuracy in real-world scenarios, utilizing attention mechanisms to precisely recognize objects based on user-provided labels and images. Developed using React Native and TypeScript, the app operates seamlessly across multiple platforms and integrates a self-hosted PostgreSQL database, ensuring data integrity and enhancing user privacy. Key functionalities include personalized nutrition profiles, real-time food scanning, and health insights, facilitating informed dietary choices for health management and lifestyle optimization. Future developments aim to integrate wearable technologies for more tailored health recommendations. Keywords: Food Image Recognition, Machine Learning in Nutrition, Zero-Shot Object Detection

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Cite this paper

@article{nossair2024eating,
  title  = {Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App},
  author = {Abdelilah Nossair and Hamza El Housni},
  year   = {2024},
  eprint = {2406.00848},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.00848v1}
}

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