Tag Archives: analysis
Jamie Williams found himself with almost two years of self-tracking data including physical activity, blood pressure, and weight. Because of his interest in data visualization and coding he decided to learn how to access it the data and work on visualizing and understanding some of the trends and patterns. In this talk, presented at the QS St. Louis meetup group, he takes a deep dive into his activity and step data as well as his blood pressure data to learn about himself and what affects his behavior and associated data.
What Did Jamie Do?
Out of pure interest in seeing what the data would reveal, Jamie utilized a combination of devices to track his physical activity, blood pressure, heart rate, weight, numbers of drinks, and automobile travel. He then went on to explore ways in which he could pull down, integrate, visualize, and ultimately make sense of what he collected.
How Did He Do It?
In order to obtain his data on a minute-level resolution, Jamie had to email FitBit for a specialized use of their API. He then employed Mathematica to develop a number of (beautiful) visualizations of his activity – along with other key moments in his life (moving to St. Louis, changing job location, preparing for a Half Marathon, etc.). Jamie was able to compare his data not only to his peers through FitBit, but also to others of his demographic in the U.S. using the publicily available NHANES data set.
What Did He Learn?
Through Jamie’s Quantified Self collection and analysis efforts, he learned a lot not only about the patterns and changes in his activity, but why they were the case. He also presented great feedback about one’s mindset when comparing to peers vs. the general population.
Withing Blood Pressure Cuff
Thank you to QS St. Louis organizer, William Dahl, and Jamie for the original posting of this talk!
Like anyone who has ever been bombarded with magazine headlines in a grocery store checkout line, Kouris Kalligas had a few assumptions about how to reduce his weight and improve his sleep. Instead of taking someone’s word for it, he looked to his own data to see if these assumptions were true. After building up months of data from his wireless scale, diet tracking application, activity tracking devices, and sleep app he spent time inputing that data into Excel to find out if there were any significant correlations. What he found out was surprising and eye-opening.
This video is a great example of our user-driven program at our Quantified Self Conferences. If you’re interest in tell your own self-tracking story, or want to hear real examples of how people use data in their lives we invite you to register for the QS15 Conference & Exposition.
We hope you enjoy this week’s list!
Are Google making money from your exercise data?: Exercise activity as digital labour by Christopher Till. Christopher describes his recent paper, Exercise as Labour: Quantified Self and the Transformation of Exercise into Labour, which lays out a compelling argument for considering what happens when all of our exercise and activity data become comparable. Are we destined to become laborers producing an expanding commercialization of our physical activities and the data they produce?
How Big is the Human Genome? by Reid J. Robinson. Prompted by a recent conversation at QS Labs, I went looking for information about the size of the human genome. This post was one of the most clear descriptions I was able to find.
Visualizing Summer Travels by Geoff Boeing. A mix of Show&Tell and visualization here. Geoff is a graduate student and as part of his current studies he’s exploring mapping and visualization techniques. If you’re interested in mapping your personal GPS data, especially OpenPaths data, Geoff has posted a variety of tutorials you can use.
Symptom Portraits by Virgil Wong. For 30 weeks Virgil met with patients and helped them turn their symptoms into piece of art work and data visualization.
Data Visualization Rules, 1915 by Ben Schmidt. In 1915, the US Bureau of the Census published a set of rules for graphic presentation. A great find by Ben here.
Lee Rogers has been collecting data about himself for over three years. The daily checkins, movements, and other activities of his life are capture by automatic and passive systems and tools. What makes Lee a bit different than most is that he’s set up a personal automation system to collect and make sense of all that data. A big part of that system is creating an annual report every year that focuses on his goals and different methods to display and visualize the vast amount of information he’s collecting. In this talk, presented at the Bay Area QS meetup group, Lee explains his data collection and why he values these annual snapshots of his life.
Eric Jain stumbled upon a study published in 2013 that found the a full moon was associated with less sleep. Being an avid self-tracker and a toolmaker he decided to find out if that was true for him as well. Eric used his tool, Zenobase, to import, aggregate, filter, and then analyze his sleep data in a few unique ways. While he found some evidence that a full moon was associated with less total sleep he wasn’t able to make any statistically significant results. Watch his short video below, filmed at the Seattle QS meetup group, then take a look at his great screencast where he walks through all his steps to complete this analysis.