AI-Powered Fitbit Health Coach
Develop an AI model to provide personalized health recommendations based on Fitbit data. The model should analyze user activity, sleep patterns, and other relevant metrics to offer tailored advice on improving fitness, sleep quality, and overall well-being. Consider incorporating features like motivational messages and progress tracking to enhance user engagement. Focus on generating actionable and relevant suggestions, avoiding generic advice.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AI model that personalizes health recommendations based on Fitbit data.
How work is evaluated
The model will be evaluated based on its accuracy in predicting health outcomes, the relevance of its recommendations, and the user-friendliness of its interface.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Evaluate the model's performance using relevant metrics.
Build a robust data preprocessing pipeline for Fitbit data.
Develop a machine learning model to predict health outcomes.
Create a user-friendly interface for presenting personalized recommendations.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The model will be evaluated based on its accuracy in predicting health outcomes, the relevance of its recommendations, and the user-friendliness of its interface.
Challenge input
JSON containing Fitbit data (activity, sleep, etc.)
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON containing personalized health recommendations and motivational messages.
- Verifies correct JSON format
- Recommendation Relevance target: 0.8
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Verifies correct JSON format
Metrics
- Recommendation Relevance · Target 0.8
- Accuracy · Target 0.7
Workflow tasks
- Task 1
Input: JSON containing Fitbit data (activity, sleep, etc.)
Output: JSON containing personalized health recommendations and motivational messages.
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.