Ann Arbor, MIUSA
October 9, 2012
Quantified Self is a movement of people who are collecting data on various aspects of their lives in order to better understand how we work and the impact of behavior/environment/other influential factors on those areas we are interested in. This data-driven understanding of self can aid in better decision making (applying the theory of "you can't manage what you don't measure" to us as individuals). It is also a community of supporters where knowledge of self can be shared and aggregated to facilitate learning. There is also an ecosystem that supports the movement by developing a variety of tools to help people capture, filter, process, interpret, and use all the self-tracking data being collected. QS pre-dates technology, but technology is a key enabler to help expand adoption by making the process easier, insights more useful, and improved results.
I am in the process of testing a software tool that analyzes time-series data (i.e. that collected by the Quantified Self) to identify and measure the relationships between variables. When combined with a structured experiment, the software can help elucidate causal relationships. I'm interested in learning more about how the Quantified Self audience analyzes and interprets their data data and if this tool can help this community get more meaning out of the data in a "quantified."
I have tracked my "Financial Self" for close to 8 years. For the past 8 months, I've been tracking my running using the MapMyRun app. I've recently downloaded a nutrition app for myself and my wife (myfitnesspal.com). I'm interested in learning about the most effective tracking/analysis tools for myself personally and for potential users who could benefit for the tool my company is developing. I'm also very interested in learning where/how I can aggregate this information in a central location which is a challenge I've found as I've started my personal health tracking journey.
I'm a recent Michigan MBA grad working to commercialize a pattern recognition technology that can help individuals systematically experiment and interpret relationships in self-tracked health data.
Very well done!