Ben Finn: Improving My Sleep

Benn Finn has been battling issues with his sleep ever since he was a teenager. His sleep was suffering from the usual problems we’ve all faced: taking too long to get to sleep, waking up too often, waking up late, and being tired during the day. He made plan to fix his issues by researching what affects sleep and then experimenting to find out what worked for him. For four months he tracked his sleep using Sleep Cycle along with 21 factors that he thought might affect his sleep. He also created a “sleep quality” score based on 5 different data points, including data from the Sleep Cycle app. In this talk, presented at the London QS meetup group, Ben describes his experiments, what he learned from analyzing his data, and how he finally ended up fixing his sleep issues. (Special thank you to Ken Snyder for his valuable work documenting the talks at QS London.)

BenFinn_Sleep
Slides are also available here.

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Meetups This Week

There will be two Quantified Self meetups this upcoming Thanksgiving week (that is, if you are in the United States).

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!

Tuesday, November 25
Houston, Texas
Vienna, Austria

Here are photos from meetups last week in Tokyo, Washington D.C. and Portland:
Tokyo600_431698740Tokyo600_431698738 Tokyo600_431698716Tokyo600_431698718 DC600_431710176PDX600_431775992

 

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What We Are Reading

Have a great time exploring these links, posts, and visualizations!

Articles
At Quantified Self, I forget I have Parkinson’s by Sara Riggare. Sara is a longtime member of our worldwide QS community and this heartfelt post about her experience at our conferences was wonderful to read. Experience the conference yourself and meet Sara at our QS15 Global Conference and Exposition. Register here 

Standards for Scientific Graphic Presentation by Jure Triglav. Jure is a doctor, developer, and researcher interested in how data is presented in the sciences. In this post he goes back in time to look at previous standards for presenting data that have largely been forgotten.

Painting with Data: A Conversation with Lev Manovich by Randall Packer. In this great interview, researcher, artist, and visualization expert, Lev Manovich, explains his latest work on exposing a window onto the world through photos posted to popular social apps.

Big Data, LIke Soylent Green is Made of People by Karen Gregory. A thoughtful essay here on automation, algorithmic living, and the change in value of human experience.

“In the production of these massive data sets, upon which the promise of “progress” is predicated, we are actually sharing not only our data, but the very rhythms, circulations, palpitations, and mutations of our bodies so that the data sets can be “populated” with the very inhabitants that animate us.”

When Fitbit Is the Expert Witness by Kate Crawford. I almost didn’t include this article in this week’s list. The story has been circulated so many times around the web this week, mostly without any real thought or examination. However, I found that Kate Crawford did a good job putting this news in context without resorting to sensationalism.

How California’s Crappy Vaccination Policy Puts Kids At Risk by Renee DiResta. A bit of a sensational title, but a great post that uses a variety of open data sources to showcase a growing concern about childhood vaccination policies in California.

Show&Tell
How I Used RescueTime to Baseline My Activity in 2014 and Set Goals for 2015 by Jamie Todd Rubin. I’ve been a big fan of Jamie’s writing since I found it earlier this year. He’s voracious self-tracker, mostly related to his tracking and understanding his writing, and this post doesn’t disappoint.

Sleeping My Way to Success with Data by Pamela Pavliscak. A great post by Pamela here about her experience starting tracking her sleep with the Sleep Cycle app. A great combination of actual data experience and higher-level thoughts on what it means to interface with personal data. I especially love this quote referencing her experience interacting with other sleep trackers,

“And they are doing the same thing that I’m doing — creating data about themselves, for themselves.”

Visualizations
IntoTheOkavango
Into the Okavango by The Office for Creative Research. A really neat interactive project by researchers, scientist, and the local community to document an expedition into the Okavango Delta in Botswana.

Strava
A Day in the Bike Commuting Life by Strava. The data science team at Strava put together a neat animation comprised of one-day of cycling commutes in San Francisco. Unsurprisingly, the Golden Gate Bridge is quite popular among cyclists.

From the Forum
Sleep tracking for new parents
Different Approach to ZEO Headband
Hello Everybody!
MyStress
New Self-Quantifier

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Bryan Ausinheiler: Diet and Digestion

BryanA_stool2

Bryan Ausinheiler was experiencing gastrointestinal issues for years and decided it was time to figure out what was causing it. By precisely controlling his diet – eating exactly the same quantities at exactly the same time – for a month and then measuring the quality of his stool in a self-designed spreadsheet he was able to create a baseline dataset to better understand his issues. Bryan then developed an experimental protocol that included “elimination and diet variations to figure out the cause of my frequent (3-5x/day) loose stools.” It turns out that “eating too many sunflower seeds was the main culprit.” Watch Bryan’s fascinating talk, presented at the Bay Area QS meetup group, to learn more about his process, and how he tackled self-experimentation and data collection.

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Heart Rate Variability and Stress: Learn from Paul LaFontaine at QS15

PLF_StressSessions

Join us at our upcoming QS15 Global Conference and Exposition on March 13-15 in San Francisco to learn how heart rate variability can indicate how relaxed or stressed you feel when meeting with other people.

PaulLFWe’re pleased to have a self described heart rate variability hacker and veteran QS’er Paul LaFontaine share how he uses heart rate variability readings to improve his effectiveness when engaging in discussion with others. Paul used off the shelf technology including a Polar Heart Rate Belt and a Heart Rate Variability Logger app to record his heart rhythms during different stressful and relaxing activities.  Once he had these baseline readings, he compared them to hundreds of hours of meetings to find patterns in the data and to pinpoint what was associated with relaxed, productive discussions or stressful, less productive interactions. Some of the source of stress may surprise you!

Paul’s Heart Rate Variability session is just one of the many hands-on, up-to-date, expertly moderated sessions we’re planning for the QS15 Global Conference and Exposition. This year, QS15 is going to be two full days of self-tracking talks, demos, and in depth discussion, followed by a third day for a grand public exposition of the latest self-tracking tools. Join us at the Fort Mason Center on the San Francisco Waterfront. We’ve made some early bird tickets available for readers of the Quantified Self blog (for a limited time): Register here!

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Quantifying in the Classroom

VL_improvingdata

Victor Lee makes data fun for kids. Victor is an assistant professor at Utah State University, where he’s been working on ways to bring the Quantified Self experience into high school classrooms to improve data literacy and expose students to “more authentic forms of inquiry.”

I first met Victor at the 2013 Quantified Self Global Conference, where we had a great conversation about QS and education. A few weeks ago, I saw this wonderful presentation of his research (a video is embedded below). I couldn’t resist the chance to ask him a few questions about his work.

If you are a teacher or a parent interested in using QS as a way to get kids into math and science, we want to especially invite you to QS15: The Quantified Self Global Conference and Exposition, where QS and Education is an important theme.

QS: What inspired you to work on data literacy as a subject area?

victor_profileVictor Lee: On the one hand, the first time I had a wearable device (a heart rate monitor, several years ago), I was impressed with the data I could receive and what kind of inferences I could make from them. This made me aware of an opportunity with the technology. At the same time, I know from my training and work as a learning scientist that data can be notoriously difficult for students (and adults) to understand and use. I have done a lot of work in the area of science education previously, and finding ways for kids to meaningfully work with and learn from data is one of the big challenges that have been documented for quite some time. Plus, the topic of data literacy is timely in that we have much greater access to data in so many forms, whether it is charts showing how the Earth’s climate is changing or if it is infographics that make the rounds on the internet. There is a lot of talk about “big data”, and if we are going to have the next generation of workers and citizens be ready to work with all of that data, we really need to work on supporting that literacy now.

QS: Your research involves students using wearable devices to generate and explore their own data. Why is having their own data an important part of this process?

VL: Informally, I talk about the benefit of wearables as helping to make it so that students have some “skin in the game”. Data that is about you is consequential because at some level, it reveals something about who you are and the things you do. In some sense, there is something inherently interesting about learning more about yourself and also comparing yourself against others or against the goals that you have set. But beyond that, there is a lot to leverage in the way of learning. One of the big ideas out of education and the learning sciences, and one that I can’t reiterate enough, is the importance of building on prior knowledge. When you look at data about yourself, you don’t strictly see points or dots or bars. You see a depiction of an experience or activity that is already intimately familiar. In some sense, you know why the data look the way that they do. If it’s exercise data, you remember what certain moments felt like. You have some expertise on how your body works and that creates some expectations and support for thinking with data. That is a really important bit of personal knowledge to leverage.

Wearables are also useful in that they expose students to some of the messier things that come along with data. You have to realize that a tracking device is doing some form of measurement and measurements are prone to some error. You get so much data that you can start to see typicality and patterns. Data are hard to collect, but being able to wear something that collects data in the background means you can collect a lot of data and see what regularity looks like. You get to see what noise looks like. You get to know what is an outlier and how that fits against the larger set of data. Too often in school, we are given these very sanitized data experiences and students don’t get to think through these things or experience them in a familiar or meaningful way.

QS: Each of your examples that you discuss during your talk were student-led and defined experiments. What role does allowing students to ask the questions play in the learning process?

VL: Students are inherently curious. They have their own interests and things they care about that speak to their experiences and their concerns. A student-centered approach to instruction tries to capitalize on that. That means looking to students for questions. It also helps to put some more skin in the game. But beyond that, it helps in learning how to actually do science. If you ask most science educators, the big goal is not to memorize facts and terms but to know how science is done and how things move from questions to data to conclusions to new questions and so on. This is critically important, and there is an even greater push for new technologies and new models in education to support this. It certainly is not easy, but I think that it is worthwhile and that the kids who get to really do it find it worthwhile too.

QS: It appears that your central thesis is “When you give them the opportunity, kids can learn hard things.” Clearly from your examples this is true. Outlier analysis, data visualization, and pattern recognition are all present. What makes the methods you’re exploring so impactful?

VL: I suspect it has to do with how the students are able to leverage their own experiences and interests. It is memorable but also consequential to something that they already do or encounter. It lets them sit in the driver’s seat when they often are put in situations that makes them more of a passenger. In fact, I think that is one of the interesting things about the Quantified Self movement. In some respects, it is increasing access to data and making it possible for people to do aspects of science or mathematics or statistics in ways that are meaningful to them.

I do also want to credit some really remarkable teachers and schools that allow for activities like this to happen. Especially in this day and age with an intense focus on testing, students don’t get as many opportunities to be curious in this way. Having motivated and flexible teachers on board certainly helps make this impactful.

QS: I was really struck by the anecdotes you shared that showed how strongly the students were affected by the lessons plans (Note: fast forward to 42:40 in the video above for a great example.) Are there any other stories that come to mind that help illustrate how students engage with these type of personal data based curriculums?

VL: We recently finished a project with a school we had never worked with previously. There were some students at this school who were really disappointed that the unit ended and they would not be able to keep working with the wearable devices that we provided (in this case, Fitbit Flex wristbands). I know one student that we worked with who was so enthusiastic that he wanted to keep on doing data collection and analysis using the kinds of tools we provided and would pull a member of my research team aside to help him plan how to keep working with data after the unit had ended. I know another student was really distraught that he was going to be absent on a day that he was set to share what he had did with activity data with the rest of the class. The teacher ended up extending things another day so that student could be there and still share his discoveries.

I may have mentioned this in the presentation, but the students who discovered outlier sensitivity were so enthusiastic about what they learned that after we shut down the cameras and were leaving, they began to boast to the other students what they had figured out and proceeded to show them how outlier sensitivity worked.

VL_quantifyingrecess

QS: You’ve done some work with trying to implement QS meetups in schools and in younger age groups. What have you learned from that experience?

VL: This has been an interesting side project. Basically, I wanted to give some students who did not have the opportunity to experience QS (in this case, some very talented and motivated high school Latina girls). Those students were terrific to work with, but the experience raised some interesting challenges and concerns about how much background infrastructure that needs to be in place to be a QSer. While they all had access to mobile devices, they were not the cutting edge. Some did not have wifi at home or bluetooth. They also felt that the latest and greatest wearable devices, while cool, didn’t fit with their aesthetic. And they had some constraints on their life circumstances that limited how much they could experiment with the devices. I presented some of these findings at a workshop as part of a recent ubiquitous computing conference. There is the potential for several potential benefits with QS being accessible to youth, but I think that this is a population with very different needs and concerns than those who are early adopters. If we want QS to be something that could be of value to youth beyond a classroom curriculum, we need to do some more targeted research and development. That’s generally something that I would be glad to pursue more in the future, as I imagine are many of my professional colleagues.

QS: Lastly, what new ideas and projects are you excited about?

VL: I am excited to do a more detailed analysis of what students learned from our most recent launch of a wearables-based data unit in the sixth grade. I am excited to potentially extend some of our findings to other grade levels and finding the best ways to address how self data could be useful in supporting rich student learning. I have been generally intrigued by the QS movement and have been trying to understand why people self-track and what they end up doing with the data they collect. There are other project in my field that I think are outstanding. At UC Davis, there is some work to get self data as input into digital games. We are actually starting to explore similar issues at Utah State. There is a neat project at CU-Boulder with kids building their own infographics, and I would love for self collected data to gradually become a part of that. I have had some side conversations with some organizations who have been thinking about wearable sensors in schools, and if those conversations continue and we are able to share what we have learned, I think there is much to be excited about in that area.

We want to thank Victor for taking the time to speak with us about his work. If you’re interested in learning more about Victor’s research we invite you to visit his faculty page and read some of this great research papers, a selection of which are linked below:

Quantified Recess: Design of an activity for elementary students involving analysis of their own movement data.
Integrating physical activity data technologies into elementary school classrooms
The Quantified Self (QS) Movement and Some Emerging Opportunities for the Educational Technology Field

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Cathal Gurrin: Me and My Log

Cgurrin_lifelog

Cathal Gurrin is a researcher at Dublin City University and the University of Tsukuba. He’s also an expert in the field of visual and data-driven lifelogging. Since 2006 he’s collected over 14 million passively collected images from different wearable cameras. Add his other sensors and he’s nearing over 1TB per year of self-tracking data. In this talk, presented at our 2014 Quantified Self Europe Conference, Cathal describes what he’s learned over the last eight years and what he’s working on in his research group including search engines for lifelogging as well as privacy and storage issues.

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Meetups This Week

We have a full set of Quantified Self meetups for the upcoming week. There will be 8 occurring in 4 countries.

Meetups featuring talks include the always excellent London group, as well as, the Washington D.C. meetup with show&tells on tracking one’s weight for over 26 years and daily routines. The Groningen meetup will feature a talk on continuous glucose monitoring and what someone found out from keeping a comprehensive log book for three months.

In Indianapolis, they’ll go over how to use Apple’s Healthkit for QS. Surely, the QS Access app will come up in discussion. The group in Tokyo will be having a group discussion to talk about what they are tracking. And Portland will have their monthly workgroup, where they will make progress on their self-tracking projects.

Tuesday (November 18)
Tokyo, Japan
Miami, Florida

Wednesday (November 19)
Dallas/Fort Worth, Texas
Washington, DC

Thursday (November 20)
Groningen, Netherlands
London, England
Portland, Oregon

Saturday (November 22)
Indianapolis, Indiana

Also, check out these photos from last week’s meetup in Warsaw:

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What We Are Reading

We hope you enjoy this week’s list of articles, posts, show&tell descriptions, and visualizations!

Articles
I’m Terrified of My New TV: Why I’m Scared to Turn This Thing On — And You’d Be, Too by Michael Price. Michael, a lawyer at the Brennan Center for Justice at the NYU School of Law, describes his experiences with his new “smart” TV. More sensors means more records being stored somewhere you might not have access to. Especially interesting when your device picks up every word you say:

“But the service comes with a rather ominous warning: ‘Please be aware that if your spoken words include personal or other sensitive information, that information will be among the data captured and transmitted to a third party.’ Got that? Don’t say personal or sensitive stuff in front of the TV.”

Public Perceptions of Privacy and Security in the Post-Snowden Era by Mary Madden. A great report from the Pew Research Internet Project. I don’t want to give away any of the juicy stats so head over and read the executive summary.

This Is What Happens When Scientists Go Surfing by Nate Hoppes. It’s not all privacy talk this week. This is a fun article exploring how new sensors and systems are being used to monitor surfers as they train and practice.

How Private Data is Helping Cities Build Better Bike Routes by Shaun Courtney. We covered the new wave of personal data systems and tools feeding data back into public institutions a bit before. Interesting to hear that more cities are investing in understanding their citizens through the data they’re already collecting.

What Do Metrics Want? How Quantification Prescribes Social Interaction on Facebook by Benjamin Grosser. Ben is most commonly known around the QS community as the man behind the Facebook Demetricator, a tool to strip numbers from the Facebook user interface. In this article, published in Computational Culture, he lays out an interesting argument for how Facebook has created a system in which the users, “reimagine both self and friendship in quantitative terms, and situates them within a graphopticon, a self-induced audit of metricated social performance where the many watch the metrics of the many.”

Show&Tell
The Cubicle Gym by Gregory Ferenstein. Gregory was overweight, overworked, and in pain. He started a series of experiments to improve his help, productivity, and wellbeing. I enjoyed his mention of using the Quantified Mind website to track cognition. If you find his experience interesting make sure to read a previous piece where he explains what happened when he replaced coffee with exercise.

Maximizing Sleep with Plotly and Sleep Cycle by Instructables user make_it_or_leave_it. A really nice step by step process and example here of graphing an making sense of Sleep Cycle data.

ToiletMatters Toilet Matters by Chris Speed. A super interesting post on what a family was able to learn by having access to data on of all things, the amount of toilet paper left on a roll and when it was being used. Don’t forget to read all the way to end so you can get to gems like this:

“[…]the important note is that the source of this data is not only personal to me, it is also owned by me. We built the toilet roll holder and I own the data. There are very few products or smart phone apps that I can say the same about. Usually I find myself agreeing to all manner of data agreements in order to get the ‘free’ software that is on offer. The toilet roll holder is then my first experience of producing data that I own and that I have the potential to begin to trade with.“

Visualizations
e-traces-lesia-trubat-01_800 E-Traces by Lesia Trubat. A beautiful and fun project by recently graduated design student, Lesia Trubat. Using adruinos and sensors places on the shoes of dances she was able to create unique visualizations of dance movement. Be sure to watch the video here.

 

jepricer Animated Abstractions of Human Data by James E. Pricer. James is an artist working on exposing self-collected data in new and interesting ways. Click through to see a dozen videos based on different types of data. The image above is a capture from a video based on genotypes derived from a 23anMe dataset.

The Great Wave of Kanagawa by Manuel Lima. Although this is an essay I’m placing it here in the visualization section because of it’s importance for those working on the design and delivery of data visualizations. Manuel uses the Great Wave off Kanagawa as a wonderful metaphor for designing how we visually experience data.

D3 Deconstructor by UC Berkeley VisLab. A really neat tool here for extracting and repurposing the data powering at D3.js based visualization.

From the Forum
myStress
Diagnosed sleep apnea. Looking for metrics pre/post treatment
Trend Research
New Self Quantifier
Memosee

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Benjamin Bolland: What I Learned from Emotion Tracking

BenjaminB_MoodQuestions

“How can I define what makes me happy and what makes me sad, what is good for me?”

In 2012 Benjamin Bolland was finishing up his undergraduate degree and working on a new start-up. He found that his moods were constantly changing and wondered if there was something he could do to make sense of them as they moved “up and down.” He began tracking his mood with an simple self-designed Google Form. Each day at 11AM and 6PM he reported his mood on a 1-10 scale and wrote a quick descriptive note. After 1.5 years of doing this nearly every day he realized that he didn’t really know what was making him happy or sad so he decided to update his Google Form to include a variety of different categories that he thought might affect his mood including physical health, sport participation, and many others. In this talk, presented at the Berlin QS meetup group, Benjamin describes his process and how he’s used this mood tracking process to be more reflective and mindful during his daily life.

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