AI-Powered Health Insights that detect trends in your WHOOP and lifestyle data — helping you understand why your body feels the way it does and how to recover faster.
PerfectHealth connects your physiological metrics (sleep, recovery, HRV, alcohol, caffeine, strain) with your lifestyle events to deliver personalized AI recovery plans.
It leverages AWS Lambda, Bedrock (Claude Sonnet 4.5), and S3 to process your data, detect trends, and generate daily insights — all visualized on a modern web dashboard.
Wearables give us numbers, not meaning.
PerfectHealth bridges the gap — turning your sleep and recovery data into clear, actionable insights like:
"You're on day 3 of low recovery — prioritize sleep and hydration to bounce back by Wednesday."
- Data Collection: A WHOOP-like dataset (stored in S3) with metrics like sleep, HRV, and recovery.
- AI Analysis: AWS Lambda loads the data and queries Bedrock (Claude Sonnet 4.5) for AI-generated health insights.
- Visualization: The frontend dashboard (HTML/CSS/JS) fetches the live insights through API Gateway and displays them beautifully.
The current version uses static sample data and allows manual date selection to preview how the analysis would appear.
In the full version, data from fitness trackers would be automatically updated each morning — running the AI analysis right after wake-up to deliver real-time recovery insights and personalized recommendations.
| Layer | Technology |
|---|---|
| Frontend | HTML, CSS, JavaScript |
| Backend | AWS Lambda (Python), API Gateway |
| AI Model | Anthropic Claude Sonnet 4.5 via AWS Bedrock |
| Storage | Amazon S3 (JSON dataset) |
| Visualization | D3.js, Matplotlib |
| Hosting (optional) | GitHub Pages / Vercel |
Clone the repo:
git clone https://github.com/vedantajwani/PerfectHealth.git
cd PerfectHealth/frontendThen open index.html in your browser to view the dashboard.