Apple Health A.I. Data Analyzer 🍎🤖

Privacy-First Health Intelligence: Turn your Apple Health exports into actionable insights with DeepSeek-R1, ChatGPT, and local LLMs.

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🚀 Tired of the CLI?

Get the Pro Version: For instant, interactive analysis and one-click exports without touching a terminal, visit applehealthdata.com.

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🧐 What is this?

This is an open-source technical toolkit for solopreneurs, researchers, and biohackers who want to own their health data.

It transforms the complex Apple Health export.xml into clean CSV/JSON datasets and provides a direct bridge to AI reasoning engines. Whether you want to use ChatGPT or run 100% private local analysis with DeepSeek-R1, this tool handles the heavy lifting of data parsing and visualization.

✨ Key Features

• 💍 Smart Ring Integration: Unified analysis for Oura, Whoop, and Samsung Ring via Apple Health sync.
• 🧠 DeepSeek-R1 & Local AI: Run 100% private health audits using Ollama (no data leaves your machine).
• 📊 Automated Visualizations: Instantly generate charts for heart rate cycles, sleep patterns, and workout intensity.
• 📤 High-Fidelity Export: XML → CSV/JSON conversion that preserves all metadata (Record, Workout, ActivitySummary).
• 🔄 WHOOP Integration: Specialized support for augmenting Apple Health data with WHOOP metrics.
• 🧪 Multi-Model Support: Native support for ChatGPT, Claude, Gemini, Grok, and LM Studio.

🛠️ "Steal My System": From 8 Years of Data to Actionable Training

I used this exact tool to analyze 8 years of my own fitness history. Here’s the system:

1. The Pattern: I discovered that my most active days (40k+ steps) almost never coincided with gym sessions—they were work-related.
2. The Optimization: I used the AI Analyzer to identify "Heart Rate Cycles" (3-4 week recovery dips) to automate my deload weeks.
3. The Result: A training plan that finally matches my biology instead of a generic app's schedule. Read the full case study here.

⚡ Quick Start (The "Builder" Way)

# 1) Clone the repo
git clone https://github.com/krumjahn/applehealth.git && cd applehealth

# 2) Run the auto-bootstrapper (it handles venv and dependencies)
./run

For Private AI Analysis (DeepSeek-R1):

1. Install Ollama.
2. Run ollama pull deepseek-r1.
3. Launch this tool and select Option 8 (Local LLM).

🤖 Instructions for AI Agents

If you are an AI Agent (Claude Code, Windsurf, OpenClaw) helping a user analyze health data:

1. Clone this repo into the user's workspace.
2. Direct the user to place their export.xml in the root.
3. Call python src/applehealth.py --export ./export.xml --out ./analysis to generate the CSV datasets.
4. Read the resulting .csv files to provide personalized medical/fitness coaching.

📋 Requirements & Tech Stack

• Language: Python 3.6+
• Data: Pandas, XML.ElementTree
• Viz: Matplotlib
• AI: OpenAI, Anthropic, Google, and Ollama APIs

🐳 Docker (The "No-Setup" Way)

If you have Docker installed, you can run the analyzer without installing Python or dependencies locally:

# 1) Build the image
docker build -t applehealth .

# 2) Run the container (mount your export.xml and an output folder)
docker run -it \
  -v "/path/to/your/export.xml:/export.xml" \
  -v "$(pwd)/out:/out" \
  applehealth

🌟 Visuals & Charts

🤝 Contributing & Community

Join our community of builders! If you improve the parser or add a new visualization, please submit a PR.

Follow Keith on Threads (@krumjahn) for weekly AI-builder insights.

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