healthai 1.4.2

Privacy-first Apple Health data analyzer powered by AI

healthai — Apple Health AI Analyzer

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

https://github.com/user-attachments/assets/98ad8fc3-ed1d-4395-80c5-eb66a8cceb61

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Installation

curl -fsSL https://raw.githubusercontent.com/krumjahn/applehealth/main/install.sh | bash

Or install directly with pipx:

pipx install healthai

Then run:

healthai

On first launch, a setup wizard walks you through choosing your AI provider (OpenAI, Claude, Gemini, Grok, Ollama, and more), setting your API key, and pointing to your export.xml. Re-run it any time with healthai --setup.

🚀 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

Recommended (install once, run anywhere):

curl -fsSL https://raw.githubusercontent.com/krumjahn/applehealth/main/install.sh | bash
healthai

Or clone and run directly:

git clone https://github.com/krumjahn/applehealth.git && cd applehealth
./run

For private local AI (DeepSeek-R1, free, no API key):

1. Install Ollama and run ollama pull deepseek-r1.
2. Launch healthai, choose Ollama in the setup wizard.

🦞 OpenClaw Support

This repo now has a published OpenClaw skill:

Install from ClawHub: Apple Health Export Analyzer

What OpenClaw does for normal users:

• check your Apple Health data quickly
• turn it into a short daily health brief
• give you simple daily tips on how to improve

The skill now supports two sources:

• the Health Data AI Analyzer Mac app if its local API is available
• this open-source applehealth export.xml workflow as a fallback

For the Mac app path, the public skill now uses read-only localhost OpenClaw endpoints and does not need to read a local token file.

If you want to use the Mac app path in a restricted OpenClaw session, run the one-time companion installer:

git clone https://github.com/krumjahn/applehealth.git
cd applehealth
python skills/apple-health-export-analyzer/scripts/install_mac_app_companion.py --restart

Then use the companion skill:

Use the health-analyzer-mac-local skill. Give me my daily health brief for today and 3 suggestions.

Use it if you want OpenClaw to:

• verify your Apple Health export setup
• generate a daily health brief from export.xml
• compare your steps and sleep over the last 7 days
• create a weekly summary with workouts and heart rate context
• help you set up a recurring daily health message

Example output:

Status
- Today was a low-activity day relative to your recent baseline.

What changed
- Steps: 2,431 vs 7-day baseline 9,993
- Workouts: 0 minutes, 0 workouts
- Sleep: insufficient data

Suggestions
1. Add one easy walk today instead of trying to catch up with a hard workout.
2. Use one fixed movement anchor, like a walk after lunch.
3. Keep effort moderate when recovery data is missing.

Example prompts:

Use the Apple Health Export Analyzer skill. Verify my setup and give me my latest daily health brief with 3 suggestions.

Use the Apple Health Export Analyzer skill. Compare my steps and sleep over the last 7 days.

Use the Apple Health Export Analyzer skill. Generate a weekly summary from my Apple Health export.

🤖 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.

🦞 Local OpenClaw Workflow

skills/apple-health-export-analyzer

The skill includes concrete scripts for:

• verifying the local repo and export.xml setup
• generating a daily Apple Health brief
• comparing recent step and sleep trends
• generating a weekly summary

Example:

python skills/apple-health-export-analyzer/scripts/check_setup.py --repo /path/to/applehealth --export /path/to/export.xml --out /path/to/analysis
python skills/apple-health-export-analyzer/scripts/daily_brief.py --repo /path/to/applehealth --export /path/to/export.xml --out /path/to/analysis

📋 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.

Join my community for updates, experiments, and AI-builder workflows.

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