How I Decoded My Apple Watch Metrics: Taking a Look At The Raw Numbers (Part 2)
Failed to add items
Add to cart failed.
Add to wishlist failed.
Remove from wishlist failed.
Follow podcast failed
Unfollow podcast failed
How I Decoded My Apple Watch Metrics: Taking a Look At The Raw Numbers (Part 2)
-
Narrated by:
-
Written by:
About this listen
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.