So I’ve been looking more into the Quantified Self Movement that I mentioned yesterday. Fascinating stuff. To me, at least.
I downloaded the app I linked to yesterday (Log Life). It actually has broader applicability than just life logging. The designer of the app (Richard Ehmer) was trying to solve a typical problem with personal data collection: it takes too long. Indeed, that’s why my earlier efforts in this direction failed. Say I want to log when my bus shows up in the morning. When the bus gets there I have to get out my phone, a pen, and my notebook; then check the time and write it down in the notebook. If I want to log my entire commute I have to do those same actions eight times. With the Log Life app, I just get out my phone, click on the app, and click on two buttons I already set up. That automatically logs what happened, when it happened, and where it happened (by hooking in to the GPS). Plus, it can easily be modified to track all my travel, whether I walk, drive, or ride my bicycle.
So the app has broader use in entering in any data quickly. He has a couple interesting examples on his website. In one case he logged various characters, events, and phrases in a TV version of Treasure Island. Not only was he able to track about 30 different things without pausing the movie, but he developed the button definitions to do so on the fly as he was watching it. He also tracked the data on the Jeopardy episodes featuring Watson. He was able to track the dollar value of each question, each attempted answer an whether it was right. (The data suggest that Watson won not because he was better at trivia, but because he was faster on the buzzer).
My favorite thing about the app is that it will export all the data to csv files. Sure, it can do basic graphs for you, but they’re all pie charts, and good God do I hate pie charts. With the csv files I can analyze the data myself. Not only that, I can combine it with other data sources. I’m already tracking my work activities pretty closely. I could pull in weather data and compare it with mood tracking on Log Life. Considering all the data available on the internet, I could probably find out which of my activities have the biggest effect on the price of tea in China.
This is not something you can do with everything I looked into. There are some very nice physical monitors you can get, like Fitbit and BodyMedia. You wear them constantly (yes, that means 24/7) and they collect data on all sorts of activities, from pedometer style step counts to galvanic skin response. From your motions while in bed they can tell when you go to bed, when you actually get to sleep, and how often you wake up in the middle of the night. But they don’t give you the data. The data collected by the monitor uploads directly to their websites, not bothering to stop off at your computer. I especially like how Fitbit says “the data is yours, we will always offer free accounts.” You can’t get the data with the free accounts, just a bunch of pie charts. You can only get the data with the premium account ($50/year) and even then they only give you summary data. I was very interested in buying one of those, but I’m not going to buy either one if I can’t have my own damn data. And what are they doing with that data? It seems to me that their business model must rest on selling that data to someone (a la facebook and Google) , but who?
A cool gadget I did find was the Vicon Revue Sensecam. You wear it around your neck and it takes pictures whenever it senses a change in motion or environment. You can use it to make a time elapse film of your whole day. It does cost about $500, but I happen to have that much handy. While it is cool, I’m not sure I want to buy it. I’m a data guy, how am I going to get data out of all those pictures? Maybe I could train a neural net to recognize certain features of the pictures…
Some of this I found through the link I posted yesterday to quantifiedself.com. But I also got some of it from a paper written on analyzing different systems for collecting this sort of data, which they call personal informatics. It’s by Li, Dey, and Forlizzi from the Human Computer Interaction Institute at Carnegie Mellon. It’s got all sorts of fascinating stuff in it, like Benjamin Franklin’s 13 virtues (which he tracked his adherence to for 60 years), Nicholas Feltron’s yearly reports on himself, and FlowingData (I’m not sure yet, but it looks to blow Information is Beautiful out of the water, not that it’s hard to do that). I haven’t finished reading it yet, but maybe I’ll have time tomorrow.
An amazing vista has opened before me, but I can’t dive into it yet. I’m doing an online habit forming course, which emphasizes one habit change at a time. I can’t dive full force into gathering mounds of data about myself and give full attention to the one habit I’m supposed to be working on. And the lesson of the habit forming course is that I shouldn’t. I’m not sure I can resist cheating, though. I may try just starting with tracking my travel and ideas (Log Life looks to be good for making short, categorized notes), but be ready to ditch it if it starts to interfere with my new habit.