Python provides a great set of built-in tools and third-party libraries for data analysis. Modern personal devices like smart watches or phones generate streams of data about body metrics, location, movement, and more. I describe Python-based methods for extracting and analyzing data from personal smart devices. I applied these methods to track and change habits and behaviors to lose 25 pounds.
Your smartwatch and smartphone provide reams of data about your body, movement, behavior, health, and more. Python is an ideal language to use for analyzing, transforming, and displaying this data. Furthermore, numerous third-party packages such as NumPy, SciPy, pandas, and matplotlib make this process easier, faster, more fun, and more insightful than ever before.
Furthermore, you can use these tools to get tangible results in your life: for example, during the first few months of 2019, I used a set of Python scripts operating on a combination of personal data sources to modify my habits and behaviors to lose 25 pounds!
In this talk we analyze several streams of data from Apple Watch and iPhone to explore what we can learn from them, individually and in combination. Data categories that we explore include:
We use simple but powerful techniques from signal processing, including moving averages and filtering, to extract insight from the data. Additionally, we investigate correlations between the different data streams.
Putting this methodology in place is fun, informative, and personally rewarding. In particular, you can use it for habit tracking, to increase self-knowledge and motivate useful habit change.