AN ACTIVITY HEATMAP FROM FITBIT DATA
The Fitbit is a very nicely designed device, basically an accelerometer plus clock in a belt clip that will wirelessly sync to a base station. It’s marketed as a fitness aid — on their site, you can check out charts and graphs that give you some sense whether you’re moving as much as you should be, set goals, and add subjectively reported measures like diet and mood. It’s a perfect example of a tool for a quantified self practice.
Though fitness is interesting, I’m more fascinated with the possibilities of using movement data for behavior analysis. My intuition is that the patterns of how much one moves over the course of a day or week or month is enough to infer quite a bit about an individual’s lifestyle and to potentially drive a predictive model.
Fitbit data, once gathered by the base station, is sent to your account on their site, which is accessible through an oauth-based API. Which is awesome. However, I was disappointed that the data they expose is very much geared toward their fitness market, and is preprocessed and interpreted along these lines. Significantly, unless I’ve just missed something, the raw, minute-by-minute data is not available, and everything is aggregated by day. While I can understand the motivation behind this, there is lot of power in the raw stream that’s not exposed.
However, prior to the release of the Fitbit API, the developer Eric Blue published a hack in perl that scrapes data from your Fitbit profile page — turns out that the Flash charts they display are powered by an XML feed with significantly more resolution than what is offered via the API. Based on this discovery, I wrote a python script that pulls the granular data.
Using numpy and my nascent drawing module, I created a heatmap of my average week in terms of intensity of movement (since 2011-01-01, wearing the Fitbit somewhat consistently). Behavior clearly emerges, and my bike to work each morning dominates.
From here, there are a lot of possibilities. Beginning to cluster days by type and to cross-reference this with other vectors, such as location, are my next steps. We’ve ordered like 10 of these things for the lab, so I’m psyched to see what we can do with them.
