Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App
<|reference_start|>Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App: 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<|reference_end|>
@article{nossair2024eating,
    title={Eating Smart: Advancing Health Informatics with the Grounding DINO based
  Dietary Assistant App},
    author={Abdelilah Nossair, Hamza El Housni},
    journal={Eating Smart: Advancing Health Informatics with the Grounding
  DINO-based Dietary Assistant App, International Journal of Scientific and
  Innovative Studies, June 2024, Volume 3, Number 3, Pages 26-34, Available
  online at IJSRIS},
    year={2024},
    doi={10.5281/zenodo.11243881},
    archivePrefix={arXiv},
    eprint={2406.00848},
    primaryClass={cs.CV}
}