While fitness trackers include some excellent tools for reviewing health data there are limitations such as overlaying data from different sources when looking for patterns. Below are two examples of graphing health data using IPython’s popular data science libraries (panda and matplotlib). In these examples I have exported my heart rate variability and resting heart rate data from the last month into two column CSV files. The data is being graphed with the Anaconda IPython ‘qtconsole’ utility.
Anaconda provides a simple installer blob which supports Linux, Windows and OS/X. This is probably the easiest all platform solution to get the core data science tools in place. Packages can be managed with conda after installation.
Once Anaconda is installed you can launch the ‘qtconsole’ within Anaconda Navigator.
Import and Graph Heart Rate Variability (HRV)
Using a CSV file with two columns (Day and HRV) you can see just how easy it is to create a simple plot of the values for the last month. See example1.py
Graph Heart Rate Variability compared to Resting Heart Rate
I have noticed that there appears to be a rough inverse correlation between heart rate variability and resting heart rate. The theory being a high HRV value is a sign of readiness for more activity and a low RHR value indicates the same. Let’ see how the two look when we overlay the data. The RHR CSV
file and example2.py
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