Home » Blog » quantified self 2011 » Quantified Self Ignite talks, part two

Quantified Self Ignite talks, part two

This post is part of my liveblogged account of a conference. Two disclaimers: Liveblogging is hard, and I often get things wrong. If I did, please feel free to correct me via email or in the comments and I’ll make changes when appropriate. Second, the opinions expressed in these sorts of posts are those of the speakers, rather than mine.

Man, Ignite talks. They’re great, but they’re overwhelming to blog. Here’s another round of nine at Quantified Self – forgive the lack of links and often, surnames – this has been an informal sort of events.

Alex Chafee from Moodlog tells us that that we’re happier than we think. That’s one of his discoveries from logging his own mood and looking at the mood daa of others. He’s working on methods to talk about mood that’s more subtle than measuring from 1-5. One tool allows people to characterize moods with colors – it turns out that hunger is a dark rust color. We might consider moods in terms of different scales and axes. One might be our feelings of sociability – we might distinguish between being lonely and being solitary in terms of how much sociability we want. Over time, the system will allow him to build a crowd-sourced scale of mood words.

Bob Evans wants to build not one, but 700 applications to track people’s moods. His platform PACO is designed to allow easy creation of new tracking applications. PACO is an acronym – personal analytics companion – but it’s also the name of his dog. And dogs are an excellent companion that are sensitive to your moods and behaviors.

The design philosophy of PACO is on building simple tools that can be connected together, ala UNIX. And there’s a basic philosophy of privacy – your data is yours, and you can share it if you’d like.

You can design a simple app, and it will ask for your attention in the tray on an Android phone. A participant can answer questions, and she can always access the data she’s entered. As someone who’s administering an experiment, you can see all the reported data.

In deploying the tool, he’s discovered that it’s important for surveys to be short and sweet. Three questions is about as much as anyone will answer. And you need to ask the right questions – a guy who put together a study that asked “What are you doing?” eight times a day was able to participate in his own experiment for only three days before he gave up!

Ian Eslick of the MIT Media Lab wants to learn from your QS experiments. There’s hundreds of possible self-optimization experiments to try, listed on the internet. As people engage in QS experiments, there’s data to evaluate. But how do you generalize from people’s self-experiments?

He sees promise in combining information from multiple studies using techniques from recommendation engines. In these systems, thousands of people’s preferences combine to predict your possible preferences. He’s working to use techniques from research on recommendation engines to allow aggregation of data from sites like Cure Together.

Mei Lin Fung tells us about weight loss study she participated in some years back. She was one of 200 participants in a study that compared two weight loss methods – lose weight and then maintain, or learn to maintain, then lose weight. She was in the group that learned to maintain weight first, for eight weeks, then focused on losing weight for 20 weeks. Her total involvement with the study was two years long, including 6 months wearing a pedometer. And for her, the most challenging part was weekly meetings of 20 women with a facilitator.

Some useful techniques she learned included landmark walks, finding local landmarks that were 1000 steps away, allowing for short, 10 minute walks towards a goal of 3000 to 6000 steps per day. Learning to articulate and track goals was helpful, as was smelling and savoring food before eating it.

Weight loss has four goals, she tells us – improved nutrition, increased physical activity, increased quality of life and weight loss, only as the fourth component. She tells us that, despite what you might learn working on your own, social support is critical. And she urges us to consider that personal and professional science can work together to make discoveries.

Uwe Erich Heiss from Dynamic Clinical Systems talks about tracking pain. Doctors ask patients, “How have you been?” People are lousy at remembering how they’ve been. And doctors give patients 22 seconds, on average, to answer that question. So it’s worth finding ways to help people track how they’ve been and get that information into the doctor’s hands in a meaningful way.

He and his team have repurposes an unsuccessful technology, the iTag – originally used to “bookmark your radio”. By clicking, you record the level of discomfort you are feeling, using multiple clicks to signify more intense pain. They’ve now moved to a browser based system that patients can use at home, drawing on a picture of a body to show a pain map. They’ve collected a million events from 100,000 patients in 20 clinical areas using the system.

While there’s much to be learned from the data collected, there’s also much to learn about the process. He tells us that experiments would work better if there were a baseline of data collected before someone is experiencing pain. We need better standards for self-tracking, and APIs and rules for sharing data.

Kyle Machulis of OpenYou brings the house down with his Quantified Coder Project. The motto – “Putting the U back in programming, even if there was no U, even in British English”.

In his day job, Kyle writes device drivers for game controllers, and now for devices like the Mindwave, Zeo, and Fitbit, allowing him to work with data locally, rather than putting it into the cloud. OpenYou is about opening all sorts of data, including from locked-down devices like pacemakers. He takes an “any means necessary” approach, which may mean violating licenses and warranties, and publishes code allowing you to open your quantitative devices as well.

All this driver development and device analysis requires lots of sitting. Like many programmers, Kyle gets into a zone, writing code for 6-14 hours at a stretch. He wondered, “What can we do with a programming environment that would help us understand what happens in those hours?”

We could use accelerometers on our chairs, measures of strikeforce on our keys to understand whether we were fidgety, or stressed when working on particular code. And we could correlate these measures to the code written. If bugs are associated with a particular pattern of physical states, maybe we could review our code by looking at other times we were frustrated or fidgety. Apply this to other people’s code and we can figure out what libraries are frustrating to use, and which other programmers enjoy. In the long run, perhaps this becomes a piece of metadata we share on services like Google Code.

Dennis Harscoat of Quan#er introduces us to a beta iphone app that asks the question, “How much?”

You can use Quan#er to post information like “#coffee: 4cups”. The idea is to ask people to post data that’s specific, open and enables discovery. The ap asks what, how much, and what unit, and allows an optional picture. Outputs include graphs of whatever you’re tracking over time. If you choose to publish your data, others can cheer you on and compare their data to yours. Alternatively, you can track privately, but their business model is “free for all, pay for privacy”. (He reminds us that we can trust him, as he and the company are Swiss. Actually, he’s French Belgian living in Switzerland, whatever that means about trust.) At the very least, we can get our data from his system via emailed text file at any time.

Ernesto Ramirez wants to make you move. He tells us that the difference between UK bus drivers, who are more prone to heart attacks, than UK ticket takers, is that the latter move and the former don’t. We get guidelines on doing 150 minutes of vigorous physical exercise a week, but the truth is, we need to move far more than 30 minutes a day. “Sitting will kill you, even if you’re physically active,” as James Levine at the Mayo Clinic has discovered.

You could walk while you work, using Steelcase’s $4400 walking treadmill desk. Or you could built your own. Ernesto bought a $100 treadmill from Craigslist and a $149 IKEA desk and built his own workstation. In 2 years, he’s logged 600 miles on it, which represents 61,000 calories or 17 pounds of weight. He shows us Fitbit data correlated with Rescue Time that demonstrates he’s done serious work while on the device.

His advice. “Just fucking do it. No, seriously, just fucking do it.” It’s not about fitness – it’s about movement, which will make you a better person.

Mark Carranza describes himself as a former poet, “because it’s much more poetic than being a poet.” And he’s a passionate explorer of memory. His system, memex.mx, has helped him record everything he’s thought since 1984. In that time, he’s recorded 1,230,348 thoughts and 7,506,340 links between thoughts. These thoughts are simple lines, a few words at a time. He writes them down on paper and has input them into a DOS computer program he hasn’t altered since 1992. He averages 232.13 new thoughts per day and 1506 new connections a day.

This collection of links and crosslinks is his hypomnema, a giant chapbook of the “accumulated treasure” of thought. He draws analogies to Vannevar Bush’s Memex system, proposed in 1945, and notes that Bush saw the human mind as unparalleled in making associations, but needing machine augmentation to improve memory. The goal of his system is building a “find engine” rather than a search engine, a form of retrieval and remembering from prompts.

We start on his DOS tool from the word “chicken”. One possible association is “epistemological chicken”, which takes us to a book by Harry M. Collins called Artificial Knowing and then to thoughts on feminist epistemology. As we go through the set of links, it’s a way for Mark to remember what he got from that experience of reading the book. Books often have hundreds or thousands of associations within the system, annotated as he’s come back to the text.

People ask whether he has any time to do anything else. He calculates that he spends no more than 45 minutes transferring thoughts from paper to computer, and that in his lifetime, he’s watched far more television, than invested time in creating an augmented memory.

A next step might be allowing you to store your thoughts in this way and an iPhone app that could allow you to compare your web of associations with others. (And I didn’t even know they had iPhones in 1992…)