Tracking with Zenobase
|Productivity, Food tracking, Sleep, Activity tracking, Diet and weight loss, Social interactions, Mood and emotion
Builds on project(s)
|Show and Tell Talk Infobox
|2013 QS Global Conference
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Tracking with Zenobase is a Show & Tell talk by Eric Jain that has been imported from the Quantified Self Show & Tell library.The talk was given on 2013/05/11 and is about Productivity, Food tracking, Sleep, Activity tracking, Diet and weight loss, Social interactions, and Mood and emotion.
Description[edit | edit source]
A description of this project as introduced by Quantified Self follows:
Eric Jain started Zenobase because he was getting fed up entering data into a dozen different, specialized tracking apps that never quite have the data fields he need, and using spreadsheets doesn't seem like much fun for him, either. Zenobase connects apps for time-tracking and self-tracking such as Foursquare, Moves, and FitBit and more.
Video and transcript[edit | edit source]
So my name is Eric Jain. I work in a very general purpose kind of time series aggregation service. It’s certainly useful for Quantified Self data. So now my short four minutes and 40 seconds to talk a bit about some of the design considerations. So here’s some of the services and devices I’m currently using, so my first problem was how do I represent all of the data that’s coming off these devices and services. So the approach I chose is to just chop up all of the data, to map it to a basic set of simple data types and to throw it all into one bucket. So we’ll look at a couple of examples.
An example here is a simple weight measurement, so we put the tags so we actually know this is a weight measurement and not like the weight of all the doughnuts I ate today. It’s the actual measurement with a unit as a time stamp and also keep track of where the data comes from. Here’s another example. It’s a basic summary data you can get off the Body Media Tracker; how active were we, how well do I sleep. You can see I put the duration how long I slept and also the sleep quality. We can get similar data off of the Fitbit. What I’m doing here is something slightly different again I get like minute by minute resolution data, so I know for each day was I sleeping, was I sitting, or was I actually moving. This is a slight different kind of sensor. It’s an indoor weather station. This is a whole bunch of interesting data types that are represented here. This is like humidity, air pressure, noise level, temperature, air quality. This here is a simple Foursquare check-in. in addition to the latitude and longitude is a link back to the restaurant and as a tag I use the Foursquare category. Now not all data is pulled in automatically, some of the data I’ll enter manually, and will have these tedious data entry forms. So here we can see I’m actually entering data for a hike, so go and input and put several tags, and there’s a link back to like a trip report and have the distance. This was a hike in the US and it’s in miles and if it was in Europe I’d probably put kilometers, this is elevation gain, location, and duration. Here’s another thing I keep track of like what movies I’ve been watching. I put the rating and link back to the internet movie database. So this is just to show that you can really represent a lot of different kinds of data. There’s another way to get data into the system is I have an API. So as an example I wrote this little app that will connect to a MindWave mobile headset reader signal and you can do like a short meditation session and you can post back the data to a system like how relaxed were you during the five minutes you were meditating. And this data isn’t actual real data. It’s just to remind me to drink some water. So another point is like you have all this data collected and you want to do something useful with it, and so you want some kind of dashboard to look at the data, and you don’t just want to have like static charts and numbers added up. You want to be able to drilldown, so these are like the data collection of all the outdoor trips, and can drilldown and only see my paddling trips. And now we may want to further drilldown and let’s say I’m only interested in paddling trips I did last year. I can go on the timeline, I can zoom in on 2012 and you can see all the rest of the dashboard is constantly updating the Apple update and log the total distances and everything and those numbers will update. You can see there was two different regions I was paddling, and we can zoom in on the Pacific North-West just to show that region. So now we have all those constraints and at any time we could back out, drop a constraint or add another constraint and any other dimension. So you can play around with the data and it’s not just like a static snapshot. Here are a couple of other interesting charts that you can see anything, like a histogram it shows like how many times was I sitting for more than one hour at a time. A plot to show which days I go swimming most often, and another thing I was working on was like scatterplots for doing correlations. So if this looks interesting to you, please come and see me and we’ll have an office hour at three O’Clock and love to talk.
About the presenter[edit | edit source]
Eric Jain gave this talk.