Splunk Your Health

The tools we use for cybersecurity monitoring can be repurposed for fascinating personal projects. Today, I want to share how I've been using Splunk—one of the most powerful SIEM tools—to analyze and visualize my personal health data from Apple Health and other fitness devices.

Why Monitor Your Health Data?

In cybersecurity, we track anomalies, look for patterns, and correlate events across systems to gain insights. These same principles can be applied to our personal health data:

• Pattern detection: Identifying trends in sleep, activity, heart rate, and other metrics
• Correlation: Finding relationships between different health markers
• Anomaly detection: Spotting unusual readings that might require attention
• Long-term analysis: Tracking progress toward fitness goals over extended periods

The average fitness app provides basic charts and summaries, but with a SIEM tool like Splunk, we can unlock much deeper analytical capabilities and customization options.

Getting Started: Extracting Your Apple Health Data

Apple Health acts as a central repository for health data collected by your iPhone, Apple Watch, and compatible third-party apps. The first challenge is getting this data into Splunk.

I've found two effective methods for this:

Method 1: Health Auto Export App

The Health Auto Export app from the App Store allows you to export your health data in formats that can be ingested by Splunk. Here's how to set it up:

1. Install the app: Download "Health Auto Export" from the App Store.
2. Configure exports: Set up JSON or CSV exports of your health data.
3. Select metrics: Choose which health categories to export (steps, heart rate, sleep, etc.).
4. Set up API Export: For automation, configure the API endpoint to send data directly to an HTTP Event Collector in Splunk.

Method 2: Cribl Stream

For those familiar with Cribl Stream (formerly LogStream), it offers a powerful intermediary that can transform and route health data to Splunk:

1. Set up Cribl Stream Cloud: Create a free instance.
2. Configure Raw HTTP Source: Set up an endpoint to receive data from your health app.
3. Create a Splunk destination: Direct the processed data to your Splunk instance.
4. Apply transformations: Use Cribl's processing capabilities to enrich and format your health data.

Creating Useful Dashboards

Once your health data is flowing into Splunk, it's time to create dashboards that provide meaningful insights. Here are some dashboard ideas I've implemented:

1. Daily Activity Overview

This dashboard provides a snapshot of daily metrics:

index=apple_health sourcetype=healthkit_activity 
| timechart span=1h avg(steps) as "Steps", avg(activeEnergyBurned) as "Calories Burned"

2. Heart Rate Analysis

Monitor heart rate trends and identify anomalies:

index=apple_health sourcetype=healthkit_heartrate 
| timechart span=10m avg(value) as avg_hr
| eval high=if(avg_hr>100, avg_hr, null())
| eval normal=if(avg_hr>=60 AND avg_hr<=100, avg_hr, null())
| eval low=if(avg_hr<60, avg_hr, null())

3. Sleep Quality Tracker

Analyze sleep patterns over time:

index=apple_health sourcetype=healthkit_sleep 
| eval date=strftime(_time, "%Y-%m-%d")
| stats sum(sleepMinutes) as total_sleep, avg(sleepQuality) as avg_quality by date
| eval sleep_hours=round(total_sleep/60,1)
| sort -date

Advanced Analytics: Correlating Health Metrics

The real power of using Splunk for health data becomes apparent when you start correlating different metrics:

Exercise Impact on Sleep

Does a high-intensity workout affect your sleep quality? Let's find out:

index=apple_health (sourcetype=healthkit_workout OR sourcetype=healthkit_sleep)
| eval date=strftime(_time, "%Y-%m-%d")
| stats sum(workoutMinutes) as exercise_minutes, sum(sleepMinutes) as sleep_minutes, avg(sleepQuality) as sleep_quality by date
| where exercise_minutes>0 AND sleep_minutes>0
| scatter exercise_minutes sleep_quality

Heart Rate Recovery

Measure how quickly your heart rate returns to normal after a workout:

index=apple_health sourcetype=healthkit_heartrate
| eval relative_time=relative_time(_time, workout_end)
| where relative_time>=0 AND relative_time<=600
| timechart span=1m avg(value) as "Heart Rate (BPM)"

Setting Up Health Alerts

Just like security monitoring, we can set up alerts for health metrics that warrant attention:

• Resting heart rate anomalies: Alert if your morning heart rate is significantly above your baseline.
• Insufficient sleep: Get notified when you've had several consecutive nights of poor sleep.
• Sedentary warnings: Create alerts for days with unusually low activity.
• Goal achievements: Positive alerts when you reach fitness milestones.

These alerts can be delivered via email, Slack, or even integrated with home automation systems.

Challenges and Considerations

While powerful, this approach does have some limitations to be aware of:

• Data privacy: Health data is sensitive. Ensure your Splunk instance is properly secured.
• Battery impact: Frequent data exports from your phone can affect battery life.
• Data consistency: Wearables might not record data consistently (like when charging).
• Integration limitations: Some third-party health apps don't fully integrate with Apple Health.

Resources for Getting Started

If you're interested in replicating this setup, here are some resources I recommend:

• Splunk Apple Health GitHub Repository - Open-source Splunk app for Apple Health data
• HealthKit App for Splunk - Official Splunk app for Apple HealthKit data
• Cribl's Guide to Apple Health Integration - Tutorial for setting up data pipelines

Conclusion

Repurposing a SIEM platform like Splunk for personal health analytics demonstrates how our cybersecurity skills can enhance other aspects of our lives. The same principles that help us monitor network health can provide powerful insights into our physical wellbeing.

By applying data correlation, anomaly detection, and visualization techniques to our health data, we gain a deeper understanding of our bodies and habits. These insights can lead to more informed lifestyle choices and a more data-driven approach to personal wellness.

Have you used Splunk or other data analytics tools for personal projects? I'm always interested in learning about creative applications of our professional skills. Feel free to reach out and share your experiences.