Tag Archives: visualization
The complex relationship between behavior and diabetes control has long been a testing ground for gathering and making sense of personal data. Doug Kanter is a Type-1 diabetic who’s been thinking about how self-tracking influences his diabetes control for a few years. While in graduate school at the Interactive Telecommunications Program (ITP) at NYU he started experimenting with visualizations that helped him understand his blood sugar and insulin dosing. In 2012 he began adding more data to his exploration in order to better understand how diet played a role in his diabetes self-management. Watch this great talk to learn more about Doug’s journey and his ongoing Databetes project.
We’ll be posting videos from our 2013 Global Conference during the next few months. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.
Today’s gallery image comes to us from Eric Jain. Eric is the creator of Zenobase a neat data aggregation and tracking system. He’s also been a great contributor to our community at meetups in Seattle, our conferences, and on the forum.
This map shows my outdoor trips in the Pacific Northwest since 2008. Red is driving, yellow is hiking or paddling. The map doesn’t just help me remember past trips, but also helps me decide what areas to explore next. The tracklogs were recorded with a Garmin GPS device, processed with a simple script and uploaded to Google Fusion Tables with additional meta data stored for each trip in my Zenobase account.
Today’s gallery image is from the co-organizer of the Chicago QS Meetup group, Mark Moschel. Mark has been experimenting with various methods of self-tracking and has even built a neat SMS-based tracking tool called Ask Me Every. You can read more about his tracking and work at the wonderful Experimentable blog.
This visualization shows 3 months of my happiness data. After reviewing it two years ago, it showed me that I was unhappy when traveling for work and, shortly after, I quit my job.
We thought it fitting to include Aaron Parecki’s great visualization of his GPS tracking logs here in our QS Gallery. If you haven’t already, you can view his great talk here, during which he describes his process.
Five years of my personal GPS logs.
A driver made a left turn from a stright-only lane right in front of me as I was proceeding straight through the intersection from my straight or left lane. I have occasionally turned on the accelerometer and gyro logging in FluxStream Capture while I drive. This time around, I have even more data. You can see the massive deceleration and the associated spike in my heart rate and drop in my beat spacing (RR). I haven’t pulled my GPS data yet, but I was able to spot this easily in the FluxStream graph. Those dips in the Acceleration data really stand out. Interestingly, my heart rate also reflects my mood afterward.
Initially relieved that I didn’t get hit this time, then enraged that it had nearly happened again, calming slowly as I composed in my head a letter to the City of Addison imploring them to add more signage at that intersection.
I gained a lot of insights from this heat map. The most obvious weight gain was no surprise — that’s when I periodically don’t track. In any case, the big picture patterns are easily identified with a heat map.
Realized looking at this heat map that the point of no return was mid-April 2012 — my data shows that was when I switched protein shakes with an egg based breakfast. I have since experimented and seen that protein shake in the morning seems to keep my blood sugar more stable and as a result my weight under control!
We invite you to take part in this project as we share our favorite personal data visualizations.If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along.
Biomimicry is an interesting topic and one that we’ve started to see creep into our Quantified Self tools and visualizations. While recovering from surgery, Pete Denman, an interaction designer at Intel, became inspired to start to explore biomimicry as a way to show data. In this short Ignite talk from our 2013 European Conference, Pete talks about his inspiration and how he’s begun testing and learning about using “beautiful mathematics” to explore visualizing data.
Thanks to Gary Wolf, we were able to find a great presentation delivered by Pete that provides a bit more detail on his excellent work:
This talk was filmed at our 2013 Quantified Self European Conference. We hope that you’ll join us this year for our 2013 Global Conference where we’ll have great talks, sessions, and discussions that cover the wide range of Quantified Self topics. Registration is now open so make sure to get your ticket today!
Typically when we think about Quantified Self and the associated collection and visualization of personal data we’re left struggling in the world of charts, graphs, and other well-worn visualizations. That’s not to disparage those of you who love spending some time tinkering in Excel. Those are valuable tools for understanding and there is a good reason we rely on them to tell us the stories of our data. It’s important to realize that those stories rooted in data aren’t always just about finding trends, searching for correlations, or teasing out significant changes. Sometimes data can represent something more visceral and organic – the expression of a unique experience.
Vincent Boyce is a an artist and designer who spends his free time riding on asphalt and water. Those experiences on his longboard and surfboard led him to starting thinking about how his rides, his performances, could be used as inputs for generating art and “exposing the hidden narrative.” After some tinkering with hardware and software Rideware Labs was born. Vincent has designed and built a prototype sensor pack and custom interface that ingests data from his riding and outputs unique visual representations. As you can see above, these aren’t your typical bar charts.
In his great talk filmed at the New York QS Meetup Vincent describes his motivation behind building his prototype system and his goals for future versions.
This is a great first step in turning data rooted in performance into artistic representations of self-expression. What do you think? What kind of data would you like to see hanging on your wall as works of art? Let us know in the comments!
If you have diabetes, or know someone who does, you’ve probably encountered a blood glucose monitor. Like many medical devices, design and data visualization are usually an afterthought. While there are many new exciting products coming to market like the iBGStar designed by Agamatrix, there are individuals who want to learn more than just their current blood glucose values. Diabetes care is also moving towards an automated and coordinated process driven by continuous blood glucose monitoring and implantable insulin pumps. These devices live on data, huge amounts of data, but what do their users know? More specifically, what do their users understand about their data, their condition, and themselves?
Doug Kanter is a designer, photographer and a student in the Interactive Telecommunications Program (ITP) at NYU. He’s also a Type-1 diabetic who has a keen interest in applying actionable design and interaction schemes to the data he gathers from his monitoring systems.
It is time to re-imagine the entire user experience of being a patient with diabetes. There is tremendous potential in applying information technology, creative design and research into behavior change into a comprehensive product for patients. Technology-based solutions are increasingly important resources in these times of skyrocketing treatment costs and lmited doctor availability.
Doug has been using his skills to better visualize and understand his own data, particularly his continuous blood glucose monitor. His first project, 7729, explored one month of his continuous blood glucose monitoring – the 7729 readings to be exact.
His second project expanded on the 7729 project to include not only his blood glucose monitoring, but also the insulin he was receiving. Insulin on Board, is based on 100 days of data collection and includes 820 insulin pump reading and 25,012 blood glucose reading. By coordinating these two data sets he was able to look for patterns and identify the efficacy of his insulin dosing.
The goal of Insulin on Board was to better understand the relationship between the insulin I take and the resulting blood sugar readings. It visualizes not simply when I take a dose of insulin, but when that insulin “kicks in.” Because insulin has a latency, it is helpful to see it actually has an effect on blood sugar. Often times I’ll take two or more doses of insulin within a few hours. Insulin on Board calculates the sum overlapping effect of these dosages.
I think patients like me could benefit massively from having improved visualizations that give you both a solid overview of how you are doing but also allow you to dial down into the details if you want.
Being a student and designer, Doug has done a great job explaining the process he takes for developing these visualizations. If you’re interesting in learning more about how he created these visualizations, what he learned, and future work you can follow along at Databetic and his blog.
Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.