NutriGuide – Personalized AI Nutritionist with LLM and Wearable Integration
Project Description
Develop an AI-empowered nutrition advisor that leverages LLMs (e.g., GPT-4 or open-source alternatives like LLaMA) to provide personalized meal plans and dietary recommendations based on user health data (e.g., heart rate, activity levels from smartwatches) and/or user's preferences.
Supervisor
ZHANG Qian
Quota
2
Course type
UROP3100
UROP3200
UROP4100
Applicant's Roles
Understand the usage of LLM and its application in a vertical domain problem space.
Applicant's Learning Objectives
1) Prototype a mobile/web app where users input goals (e.g., weight loss, muscle gain) and sync wearable data from smart watch or other wearables.
2) Use LLMs to generate context-aware responses (e.g., "Your elevated heart rate suggests stress; avoid caffeine post-6 PM").
3) Validate suggestions via expert review or existing guidelines.
2) Use LLMs to generate context-aware responses (e.g., "Your elevated heart rate suggests stress; avoid caffeine post-6 PM").
3) Validate suggestions via expert review or existing guidelines.
Complexity of the project
Moderate