Eating Smart: Advancing Health Informatics with the Grounding DINO based Dietary Assistant App
| Autor: | Nossair, Abdelilah, Housni, Hamza El |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | 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 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.5281/zenodo.11243881 |
| Popis: | 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 Comment: The work presented in this paper was part of the proceedings for the First International Conference on Artificial Intelligence (ICATA 2024) |
| Databáze: | arXiv |
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