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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.
We’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!
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 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.
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
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.
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.
Saturday (November 22)
Also, check out these photos from last week’s meetup in Warsaw:
We hope you enjoy this week’s list of articles, posts, show&tell descriptions, and visualizations!
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.”
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.
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.“
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.
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.
“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.
It’s commonly believed that we sleep away approximately a third of our lives. Is it good sleep? Does it help us refresh and regenerate? What can we do to make our time spent in bed even better?
Join us at our upcoming QS15 Global Conference and Exposition on March 13-15 in San Francisco to learn first-hand about how to track your sleep and benefit from your sleep data.
We’re excited to have our QS Washington DC meetup organizer, PhD student, and avid self-tracker, Daniel Gartenberg, sharing his deep knowledge of tracking sleep. Daniel has used multiple devices to find out what works and what helps him achieve a better night’s sleep, including the Sleep Smart Alarm Clock, Galaxy Gear watch, the Actiwatch (a research validated devices), and the Hexoskin shirt. We may even get a peek at the sleep tracking capacities of the Apple Watch.
Daniel’s Sleep Tracking 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!
This week we have five meetups in four countries! The meetup in Warsaw will feature a show&tell on what can be learned from combining one’s genetic and microbiome data. The first QS group, Bay Area, will be having their meetup in Berkeley and will feature talks about running data and memorizing personal events.
After two very successful events, Vienna will be hosting their third show&tell. Copenhagen will feature a talk by Mette Dyhrberg, who once provided us with this excellent visualization. The Vancouver group is teaming up with a hardware-focused meetup for presentations on the world of personal data sensors.
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!
Monday (November 10)
Wednesday (November 12)
Friday (November 14)
Vancouver, British Columbia
We had a lot of fun putting together this week’s list. Enjoy!
A Spreadsheet Way of Knowledge by Steven Levy. A few weeks ago we noted that it was the 35th anniversary of the digital spreadsheet. Steven Levy noticed too and dug up this piece he wrote for Harpers in 1984. If you read nothing else today, read this. First, because we should know where our tools come from, their history and inventors. And second, but not last nor least, because it has wonderful quotes like this:
“The spreadsheet is a tool, and it is also a world view — reality by the numbers.”
The Ethics of Experimenting on Yourself by Amy Dockser Markus. With new companies cropping up to help individuals collect and share their personal data there has been an increased interest in citizen science. A short piece here at the Wall Street Journal lays the groundwork for what may become a contentious debate between the old vanguards of the scientific institution and the companies and citizens pushing the envelope. (The article is behind a paywall, but we’ve archived it here.)
Better All The Time by James Surowiecki. I started reading this thinking it would be another good piece about the digitization of sport performance and training, and it was, but only partly. What begins with sports turns into a fascinating look at how we are succeeding, and in some cases failing, to improve.
Article 29 Data Protection Working Party: Opinion 8/2014 on the Recent Developments on the Internet of Things. Do not let the obscure boring title fool you, this is an important document, especially if you’re interested in personal data, data privacy, and data protection rights. Most interesting to me was the summary of six challenges facing IoT data privacy and protection. I’m also left wondering if other countries may follow the precedents possibly set by this EU Working Party.
30 Little-Known Features of the Health and Fitness Apps You Use Every Day by Ash Read / AddApp. Our friends at AddApp.io put together a great list of neat things you may or may not know you can do with various health and fitness apps.
Man Uses Twitter to Augment his Damaged Memory by John Paul Tiltow. Wonderful piece here about Thomas Dixon, who uses Twitter to help document his life after suffering a traumatic brain injury that severely diminished his episodic memory. What makes it more interesting is that it’s not just a journal, but also a source of inspiration for personal data analysis:
”Sometimes if I have like an hour, I’ll be like ‘How’s the last week been?’“ Dixon says. ”I’ll look at the past week and I’ll go, ‘Oh, okay. I really do want to get a run in.’ So I will use it to influence certain decisions.”
Patients and Data – Changing roles and relationships by David Gilbert and Mark Doughty. Another nice article about the ever-changing landscape that is the patient/provide/insurer ecosystem.
The Quantified Anatomy of a Paper by Mohammed AlQuaraishi. Mohammed is a Systems Biology Fellow at Harvard Medical School, and he’s an avid self-tracker. In this post he lays out what he’s learned through tracking the life of a successful project, a journal publication (read it here), and how he’s applying what he learned to another project.
Calories In, Calories Out by (author unknown). A fascinating post about modeling weight reduction over time and testing to see if said model actually matches up with recorded weight. Not all math and formulas here though,
“I learned several interesting things from this experiment. I learned that it is really hard to accurately measure calories consumed, even if you are trying. (Look at the box and think about this the next time you pour a bowl of cereal, for example.) I learned that a chicken thigh loses over 40% of its weight from grilling. And I learned that, somewhat sadly, mathematical curiosity can be an even greater motivation than self-interest in personal health.”
Fitness Tracker on a Cat – Java’s Story by Pearce H. Delphin. A delightful post here about tracking and learning about a cat’s behavior by making it wear at Fitbit. Who said QS has to be serious all the time?!
100 Days of Quantified Self by Matt Yancey. Matt downloaded his Fitbit Flex data using our data export how-to then set out analyzing and visualizing the data. Make sure to click through for the full visualization.
IAMI by Ligoranoreese. If you’re in San Francisco consider stoping by the Catherine Clark Gallery for this interesting exhibit. The duo, Ligoranoreese, created woven fiber optic artwork based on Fitbit data.
From the Forum
Anyone have a good way to aggregate and visualize data?
Questions about personal health tracking
Call for Papers: special issue of JBHI on Sensor Informatics
Sleep Tracking Device – BodyEcho
On October 23rd, the QS Stockholm meetup group meetup collaborated with the Bionyfiken, a Swedish biohacking meetup, to host a meeting at the Karolinska Institute. We’re happy to share a recap from Mina Makar and Dina Titkova, a member and co-organizers of QS Stockholm .
The meetup was conducted in a very relaxed atmosphere starting with a small introduction by the organizers followed by a short video of Gary Wolf introducing Quantified Self. We then had a few presentations from our group members.
Tina Zhu is a PhD student at KTH with focusing on Biofeedback. She talked about her interesting project of visualizing self-tracking data in the form of a fish in an aquarium. It was very interesting to see such data representation being taken to another level which could be easier for some to accept, understand, and interact with. Learn more about her work here: http://bodyandnature.
Glenn Bilby is an experienced sport medicine specialist then took the audience on a journey of all the gadgets that he has been using over the last 20 years to keep track of his different activities. During his presentation, Glenn discussed different topics related to how to use the data generated from the different devices and concluded that there is still much to be done when it comes to adding more meaning to the data.
Fredrik Bränström and Tom Everitt started a very interesting platform using a new algorithm to visualize data and highlight links between variables. This method is designed to give users a different perspective on how their daily activities are associated to each other. Their platform is available for testing here: www.kaus.se.
Sina Amoor Pour presented his reflections on what biofeedback and biohacking are and background on the Bionyfiken group.
Chai demonstrated how different chips could be implemented into the human body in order to perform different activities. He spoke about the wide range of applications for such chips ranging from starting your own car or bike.
If you’re based in Sweden, don’t miss the upcoming QS Stockholm meetups for more inspiration and ideas.