How I Decoded My Apple Watch Metrics: Taking a Look At The Raw Numbers (Part 2)
From Data Science Tech Brief By HackerNoon by HackerNoon
May 9, 2026 · 4 min
About this episode
The episode discusses how to decode Apple Watch metrics by parsing health data files using Python.
This story was originally published on HackerNoon at: https://hackernoon.com/how-i-decoded-my-apple-watch-metrics-taking-a-look-at-the-raw-numbers-part-2 . Learn how to parse Apple Health XML & GPX files. A technical guide to "streaming" large CDA files and extracting workout kinematics using Python. Check more stories related to data-science at: https://hackernoon.com/c/data-science . You can also check exclusive content about #data-science , #python-notebook , #python , #apple-watch , #apple-health , #prediction-delta , #health-data , #apple-wearable-data , and more. This story was written by: @farzon . Learn more about this writer by checking @farzon's about page, and for more stories, please visit hackernoon.com . Exporting Apple Health data results in massive, messy XML files that are difficult to process. By using a "streaming" parser to filter specific LOINC codes and extracting GPS kinematics from GPX files, I converted 300MB of raw records into clean CSVs. This structured data is now ready to be fed into a custom machine learning model to reverse-engineer VO2 Max.
People in this episode
Host: HackerNoon
Topics covered
- Apple Watch metrics
- data parsing
- health data
- Python programming
- machine learning
- workout kinematics
Keywords
- Apple Watch
- Apple Health
- Python
- data parsing
- CDA files
- GPX files
- VO2 Max
- health data
- machine learning
Mentioned in this episode
Organizations: HackerNoon
Products: Apple Watch, Apple Health, Python, CDA files, GPX files
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