Transforming Food Logging with AI: Streamlining Dietary Tracking for Better Health Outcomes with Vineeth Ramesh

Food logging is a critical component of managing metabolic health, yet traditional methods often involve manual entry of meal details, which can be time-consuming and prone to inaccuracies. Users are expected to input each ingredient and portion size, a process that can discourage consistent use and lead to incomplete or incorrect data. Accurate dietary tracking is essential for effective health management, emphasizing the need for innovative solutions to streamline food logging while maintaining accuracy.

To address these challenges, our project introduced advanced machine learning techniques to automate and simplify the food logging process. By utilising sophisticated language models and quantization techniques, we developed a system that can accurately identify ingredients from user inputs provided in natural language, whether through voice commands or casual text. We implemented the Levenshtein Distance metric to measure the similarity between user inputs and database entries, ensuring precise ingredient matching. Additionally, when exact matches were not found, a quantised model was employed to generate relevant results, effectively handling diverse and complex food entries. This approach also facilitated the creation of a structured food database, providing valuable insights into food behaviour patterns and improving our ability to analyse and study dietary habits.

Enhancing food logging methods is crucial as we advance towards more efficient and user-friendly health management tools. The ability to log meals using voice and casual text not only improves user experience but also encourages more accurate tracking of dietary intake. Moreover, the creation of a structured food database supports the study of individual food behaviour, offering deeper insights into dietary patterns. This project highlights the importance of leveraging cutting-edge technology to address real-world challenges, ultimately improving health outcomes and contributing to the broader field of digital health.